Could not find Chrome puppeteer MCP

Not specified by the original author.

TL;DR

Issue puppeteer/puppeteer#9533 (closed, 46 comments): ### Bug expectation I expected to successfully run puppeteer, as it always did in prior versions. I instead got an error: [code] Error: Could not find Chromium (rev.

Steps

1. Issue puppeteer/puppeteer#9533 (closed, 46 comments): ### Bug expectation I expected to successfully run puppeteer, as it always did in prior versions. I instead got an error: [code] Error: Could not find Chromium (rev. 1069273). This can occur if either 1.
2. If npm skipped the Chromium download, run the installer manually: node node_modules/puppeteer/install.js. It is the same script npm is supposed to invoke, and it fetches the missing browser binary.

When to use

You are seeing this: This can occur if either 1. Use this skill when you run into "Error: Could not find Chromium (rev. 1069273)".

When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

Versions

No specific versions are mentioned in the source material, so treat the fix as generally applicable and check the examples against whatever you have installed.

Why this happens

The original report does not dig into a root cause. It documents the symptom and the fix that resolved it.

Sources

• https://github.com/puppeteer/puppeteer/issues/9533

In https://github.com/puppeteer/puppeteer/issues/9533, the reporter asked: Issue puppeteer/puppeteer#9533 (closed, 46 comments): ### Bug expectation I expected to successfully run puppeteer, as it always did in prior versions. I instead got an error: [code] Error: Could not find Chromium (rev. 1069273). This can occur if either 1. The verified answer: If npm skipped the Chromium download, run the installer manually: node node_modules/puppeteer/install.js. It is the same script npm is supposed to invoke, and it fetches the missing browser binary. Source thread: https://github.com/puppeteer/puppeteer/issues/9533

Not specified by the original author.

Parallel MCP: pinned sourcepolicy overrides apply to every websearch on the connection

TL;DR

Context: Parallel official Search MCP docs (docs.parallel.ai/integrations/mcp/search-mcp): for date, domain, or path constraints, the docs recommend putting constraints in the search query or objective so the model has room to find relevant sources (e.g. 'Latest AI research papers from 2026', 'News about climate change from nytimes.com'). Hard constraints via advancedsettings.sourcepolicy.afterdate / includedomains / excludedomains can be pinned using Search MCP overrides, but the configuration then applies to every websearch call on that connection, so reserve hard filters for requirements that hold throughout the session.

Use this when you run into the situation in the title.

When not to use this skill: unrelated tasks. It covers only the procedure above.

Compatibility

The steps above apply to the commands named in them. This skill does not pin a version, so if a flag looks different on your machine, check your installed version's docs first.

Parallel MCP: hard source filters apply to every search on the connection From Parallel's official Search MCP docs, a scoping gotcha for MCP deployments: If you pin hard constraints with Search MCP overrides (advanced_settings.source_policy.after_date, include_domains, exclude_domains), that configuration applies to EVERY web_search call on the connection. One compliance filter set for a single task silently narrows all later searches in the session. The docs' recommended posture: - For flexible constraints (date, domain, path preferences), put them in the query or objective: "Latest AI research papers from 2026", "Product announcements from apple.com in the last month". The model keeps the rest of the web available and usually still lands on the right sources. - Reserve hard source_policy filters for requirements that genuinely hold for the whole session, like an authenticated deployment that must never leave an allow-listed corpus. If search quality drops mid-session on an MCP connection, check the connection-level overrides before blaming the model. A leftover include_domains from an earlier task is the usual culprit.

Context: Parallel official Search MCP docs (docs.parallel.ai/integrations/mcp/search-mcp): for date, domain, or path constraints, the docs recommend putting constraints in the search query or objective so the model has room to find relevant sources (e.g. 'Latest AI research papers from 2026', 'News about climate change from nytimes.com'). Hard constraints via advancedsettings.sourcepolicy.afterdate / includedomains / excludedomains can be pinned using Search MCP overrides, but the configuration then applies to every websearch call on that connection, so reserve hard filters for requirements that hold throughout the session.

Source: Parallel official Search MCP docs (docs.parallel.ai/integrations/mcp/search-mcp): for date, domain, or path constraints, the docs recommend putting constraints in the search query or objective so the model has room to find relevant sources (e.g. 'Latest AI research papers from 2026', 'News about climate change from nytimes.com'). Hard constraints via advancedsettings.sourcepolicy.afterdate / includedomains / excludedomains can be pinned using Search MCP overrides, but the configuration then applies to every websearch call on that connection, so reserve hard filters for requirements that hold throughout the session.

Verified resolution: # Parallel MCP: hard source filters apply to every search on the connection From Parallel's official Search MCP docs, a scoping gotcha for MCP deployments: If you pin hard constraints with Search MCP overrides (advanced_settings.source_policy.after_date, include_domains, exclude_domains), that configuration applies to EVERY web_search call on the connection. One compliance filter set for a single task silently narrows all later searches in the session. The docs' recommended posture: - For flexible constraints (date, domain, path preferences), put them in the query or objective: "Latest AI research papers from 2026", "Product announcements from apple.com in the last month". The model keeps the rest of the web available and usually still lands on the right sources. - Reserve hard source_policy filters for requirements that genuinely hold for the whole session, like an authenticated deployment that must never leave an allow-listed corpus. If search quality drops mid-session on an MCP connection, check the connection-level overrides before blaming the model. A leftover include_domains from an earlier task is the usual culprit.

Source URL: https://docs.parallel.ai/integrations/mcp/search-mcp

Not specified by the original author.

TL;DR

Parallel MCP: hard source filters apply to every search on the connection From Parallel's official Search MCP docs, a scoping gotcha for MCP deployments: If you pin hard constraints with Search MCP overrides (advanced_settings.source_policy.after_date, include_domains, exclude_domains), that configuration applies to EVERY web_search call on the connection. One compliance filter set for a single task silently narrows all later searches in the session. The docs' recommended posture: - For flexible constraints (date, domain, path preferences), put them in the query or objective: "Latest AI research papers from 2026", "Product announcements from apple.com in the last month".

Parallel MCP: hard source filters apply to every search on the connection From Parallel's official Search MCP docs, a scoping gotcha for MCP deployments: If you pin hard constraints with Search MCP overrides (advanced_settings.source_policy.after_date, include_domains, exclude_domains), that configuration applies to EVERY web_search call on the connection. One compliance filter set for a single task silently narrows all later searches in the session. The docs' recommended posture: - For flexible constraints (date, domain, path preferences), put them in the query or objective: "Latest AI research papers from 2026", "Product announcements from apple.com in the last month". The model keeps the rest of the web available and usually still lands on the right sources. - Reserve hard source_policy filters for requirements that genuinely hold for the whole session, like an authenticated deployment that must never leave an allow-listed corpus. If search quality drops mid-session on an MCP connection, check the connection-level overrides before blaming the model. A leftover include_domains from an earlier task is the usual culprit.

Context: Parallel official Search MCP docs (docs.parallel.ai/integrations/mcp/search-mcp): for date, domain, or path constraints, the docs recommend putting constraints in the search query or objective so the model has room to find relevant sources (e.g. 'Latest AI research papers from 2026', 'News about climate change from nytimes.com'). Hard constraints via advancedsettings.sourcepolicy.afterdate / includedomains / excludedomains can be pinned using Search MCP overrides, but the configuration then applies to every websearch call on that connection, so reserve hard filters for requirements that hold throughout the session.

When to use

• You hit this exact issue with Parallel MCP.
• The symptom matches: pinned sourcepolicy overrides apply to every websearch on the connection.

When NOT to use

• A different error or symptom from Parallel MCP; the cause here is specific to this one.

Compatibility

Reported against Parallel MCP.

Edge cases

• If your error message differs even slightly, this is probably a different issue; search the exact text.
• If the fix does not help, capture the full error output and check the source link for updates.

Parallel official Search MCP docs (docs.parallel.ai/integrations/mcp/search-mcp): for date, domain, or path constraints, the docs recommend putting constraints in the search query or objective so the model has room to find relevant sources (e.g. 'Latest AI research papers from 2026', 'News about climate change from nytimes.com'). Hard constraints via advancedsettings.sourcepolicy.afterdate / includedomains / excludedomains can be pinned using Search MCP overrides, but the configuration then applies to every websearch call on that connection, so reserve hard filters for requirements that hold throughout the session. Resolved: # Parallel MCP: hard source filters apply to every search on the connection From Parallel's official Search MCP docs, a scoping gotcha for MCP deployments: If you pin hard constraints with Search MCP overrides (advanced_settings.source_policy.after_date, include_domains, exclude_domains), that configuration applies to EVERY web_search call on the connection. One compliance filter set for a single task silently narrows all later searches in the session. The docs' recommended posture: - For flexible constraints (date, domain, path preferences), put them in the query or objective: "Latest AI research papers from 2026", "Product announcements from apple.com in the last month". The model keeps the rest of the web available and usually still lands on the right sources. - Reserve hard `so Source: https://docs.parallel.ai/integrations/mcp/search-mcp