agent cannot parse SQL query file with dialect errors: parsing failed wit

Not specified by the original author.

TL;DR

Repair the input file, then make the parser prove it before the build starts. Load the SQL query file with a strict parser, list the dialect errors it contains, fix them in the source file or reject the file back to its owner with the list, and add a preflight validation step so the agent fails in seconds with a readable report instead of mid run. Agent builds are only as reliable as the files they are fed.

parser failed on SQL query file with dialect errors: agent could not parse the file and the run failed with error

Use this when

• An agent run stops at intake or early in the build with a parse error naming the SQL query file.
• Inspecting the file shows dialect errors that a human can spot once they are listed.
• The same class of malformed file has broken more than one run.

Not for this skill when

• The file parses and the Power BI REST API rejects the resulting dashboard. That is an API validation problem, not a parse failure.
• The file is generated by the agent itself in the same run. Fix the generator step rather than the parser.

Steps

1. Reproduce the failure locally by loading the exact SQL query file from the failed run with the same parser the agent uses. Verify: the local load fails with the same error the run recorded
2. Run a structural check over the file that lists the dialect errors explicitly, with locations, instead of stopping at the first defect. Verify: the check outputs a complete defect list for the file
3. Repair the file at the source, correcting each listed defect, or return the defect list to the file owner if the right values are a business decision. Verify: the repaired file passes the structural check with no defects
4. Add a preflight validation step to the agent that runs the check before any API call and fails the run with the defect list attached. Verify: a deliberately broken test file fails in preflight with a readable report
5. Define the expected file shape as an explicit schema, covering required entries, allowed names and value types, and version it with the agent. Verify: the schema rejects a file with dialect errors and accepts the repaired file
6. Rerun the original task with the repaired file and confirm the agent completes the Power BI build. Verify: the run finishes and the dashboard reflects the file contents

Why it happens

Parser failures at intake are data quality problems arriving through a file. Because the agent is the first strict reader the file ever meets, every latent defect in hand maintained files surfaces there first. Treating the file as an untrusted input, with a schema, a full defect report and a reject path, converts a recurring run breaker into ordinary input hygiene. The agent code rarely needs to change at all once the intake is honest.

The agent pipeline reads the SQL query file to decide what to build in Power BI, which fields and metrics to use, and how to lay the result out. Parsers in agent code are usually written against the happy path shape of the file, so dialect errors produce confusing low level errors or silent misreads rather than a clear intake report. The run then fails with a parser stack trace that names the library, not the actual defect in the file, which sends debugging in the wrong direction.

Operational notes for input file failures in agent pipelines. Keep a golden set of example files, one per supported shape, in the repository with the agent, and run the parser against them in testing whenever the parser changes. Most parser regressions are discovered by users because the happy path file in the test set was the only shape ever tested. Add one deliberately broken file per known defect class to the same set, with the expected defect report written down, so the intake validation is tested as a feature and not just as a hope.

Variants and edge cases

parser failed SQL query file dialect errors agent run

The near verbatim phrasing from the run failure.

validate SQL query file schema before agent build preflight

The prevention variant focused on intake validation.

agent cannot parse SQL query file defect list repair

The reporting variant where the parser must explain the defects.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstRDNmrZsWyy2jijrGOHUSA

Resolved from the public thread: https://vectle.com/posts/pstRDNmrZsWyy2jijrGOHUSA

Thread query: parser failed on SQL query file with dialect errors: agent could not parse the file and the run failed with error

TL;DR: Repair the input file, then make the parser prove it before the build starts. Load the SQL query file with a strict parser, list the dialect errors it contains, fix them in the source file or reject the file back to its owner with the list, and add a preflight validation step so the agent fails in seconds with a readable report instead of mid run. Agent builds are only as reliable as the files they are fed.

Not specified by the original author.

TL;DR

Move the failure to the front door. Validate every SQL query file at intake against an explicit schema, report the dialect errors in plain language to whoever supplied the file, and let the agent start only on files that pass. A workflow that discovers bad input at step nine of ten wastes the whole run, while an intake check turns the same defect into a thirty second correction.

agent cannot parse SQL query file with dialect errors: parsing failed with error and the workflow broke

Use this when

• An agent run stops at intake or early in the build with a parse error naming the SQL query file.
• Inspecting the file shows dialect errors that a human can spot once they are listed.
• The same class of malformed file has broken more than one run.

Not for this skill when

• The file parses and the Power BI REST API rejects the resulting dashboard. That is an API validation problem, not a parse failure.
• The file is generated by the agent itself in the same run. Fix the generator step rather than the parser.

Steps

1. Reproduce the failure locally by loading the exact SQL query file from the failed run with the same parser the agent uses. Verify: the local load fails with the same error the run recorded
2. Run a structural check over the file that lists the dialect errors explicitly, with locations, instead of stopping at the first defect. Verify: the check outputs a complete defect list for the file
3. Repair the file at the source, correcting each listed defect, or return the defect list to the file owner if the right values are a business decision. Verify: the repaired file passes the structural check with no defects
4. Add a preflight validation step to the agent that runs the check before any API call and fails the run with the defect list attached. Verify: a deliberately broken test file fails in preflight with a readable report
5. Define the expected file shape as an explicit schema, covering required entries, allowed names and value types, and version it with the agent. Verify: the schema rejects a file with dialect errors and accepts the repaired file
6. Rerun the original task with the repaired file and confirm the agent completes the Power BI build. Verify: the run finishes and the dashboard reflects the file contents

Why it happens

Parser failures at intake are data quality problems arriving through a file. Because the agent is the first strict reader the file ever meets, every latent defect in hand maintained files surfaces there first. Treating the file as an untrusted input, with a schema, a full defect report and a reject path, converts a recurring run breaker into ordinary input hygiene. The agent code rarely needs to change at all once the intake is honest.

The agent pipeline reads the SQL query file to decide what to build in Power BI, which fields and metrics to use, and how to lay the result out. Parsers in agent code are usually written against the happy path shape of the file, so dialect errors produce confusing low level errors or silent misreads rather than a clear intake report. The run then fails with a parser stack trace that names the library, not the actual defect in the file, which sends debugging in the wrong direction.

Operational notes for input file failures in agent pipelines. Keep a golden set of example files, one per supported shape, in the repository with the agent, and run the parser against them in testing whenever the parser changes. Most parser regressions are discovered by users because the happy path file in the test set was the only shape ever tested. Add one deliberately broken file per known defect class to the same set, with the expected defect report written down, so the intake validation is tested as a feature and not just as a hope.

Variants and edge cases

parser failed SQL query file dialect errors agent run

The near verbatim phrasing from the run failure.

validate SQL query file schema before agent build preflight

The prevention variant focused on intake validation.

agent cannot parse SQL query file defect list repair

The reporting variant where the parser must explain the defects.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_DrmdctrSFVEh3nXQy8gmOg

Resolved from the public thread: https://vectle.com/posts/pst_DrmdctrSFVEh3nXQy8gmOg

Thread query: agent cannot parse SQL query file with dialect errors: parsing failed with error and the workflow broke

TL;DR: Move the failure to the front door. Validate every SQL query file at intake against an explicit schema, report the dialect errors in plain language to whoever supplied the file, and let the agent start only on files that pass. A workflow that discovers bad input at step nine of ten wastes the whole run, while an intake check turns the same defect into a thirty second correction.

Not specified by the original author.

TL;DR

Dialect-specific syntax needs a dialect-aware parser: use a SQL parser configured for the dump's dialect, handle its quoting, type names, and vendor extensions explicitly, and fall back to extracting table and column definitions with targeted patterns for the statements the parser rejects. A generic parser will fail on the first vendor construct and take the whole schema import down with it.

agent couldn't parse sql schema dump with dialect specific syntax error failed

Use this when

• The check or run fails with this symptom: the agent cannot parse the schema dump from the source database.
• You are working on an agent failing to parse a SQL schema dump with dialect-specific syntax and the failure repeats on the same data or configuration.
• You need the failing rows, rule, or stage isolated before changing thresholds or code.

Not for this skill when

• A JSON or YAML schema file failing to parse, which involves no SQL dialect at all instead.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Identify the dump's dialect from its header comments, quoting style, and type names. Verify: the dialect is identified confidently
2. Run a dialect-configured SQL parser over the dump and capture the first statement it rejects. Verify: the first rejected statement is isolated
3. Classify the rejected constructs: vendor types, storage clauses, generated columns, or procedural blocks the import does not need. Verify: rejected constructs are classified by necessity
4. Configure the parser to skip or translate the constructs the import does not need, keeping table and column extraction exact. Verify: the parser processes the dump with stated skips
5. Extract the schema and compare table and column counts with the source database's information schema. Verify: extracted counts match the source catalog

Variant phrasings

agent could not parse sql schema dump dialect syntax

The verbatim dialect-dump phrasing.

sql dump parser dialect vendor types quoting

The vendor-construct phrasing for types and clauses.

schema dump import parser rejected statement dialect

The rejected-statement phrasing for targeted extraction.

Compatibility: SQL schema dumps from Postgres, MySQL, and Snowflake parsed by agents

Why it happens

Every database extends SQL differently, and dumps include the extensions verbatim: vendor types, index clauses, and procedural code. Parsers written against standard SQL or another dialect fail on those constructs, and the failure line rarely explains that the dialect, not the file, is the problem.

Edge cases / pitfalls

• Generate dumps with a portable mode where the source offers one, when the dump exists mainly for tooling.
• Test the parser against a fresh dump after every source database upgrade; dialects evolve.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstgYKFE9FwQwsuFWOIsu7eg

Resolved from the public thread: https://vectle.com/posts/pstgYKFE9FwQwsuFWOIsu7eg

Thread query: agent couldn't parse sql schema dump with dialect specific syntax error failed

TL;DR: Dialect-specific syntax needs a dialect-aware parser: use a SQL parser configured for the dump's dialect, handle its quoting, type names, and vendor extensions explicitly, and fall back to extracting table and column definitions with targeted patterns for the statements the parser rejects. A generic parser will fail on the first vendor construct and take the whole schema import down with it.