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find_known_issues

Surface known UNRESOLVED problems matching a free-text description: forum threads with zero replies but high views, plus open GitHub issues. Answers "is anyone else hitting this?". NEAR-specific. Does NOT return fixes, solutions, config, or how-to steps, and returns nothing when no open issue matches; for "how do I fix / configure / why does X happen" use semantic_search (then get_doc) instead.

Input Schema

NameRequiredDescriptionDefault
limitNoMax rows per source (default 5).
descriptionYesWhat you're seeing, in short prose. For an error message, explain_error answers with its cause and fix.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations supplied, the description carries the full behavioral burden and does most of it well: it names the two upstream sources, declares that unresolved/open status is the filter, and warns that no match yields nothing. What is missing is the shape of a returned row and whether results are ranked or capped beyond the limit parameter, so the agent cannot fully predict output structure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action and sources, then exclusions and the alternative route, which is the right ordering for an agent scanning for relevance. It is dense with clauses and slightly repetitive on the fixes/how-to exclusion, but nearly every sentence adds routing or scope information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter read tool with no output schema and no annotations, the description covers scope, sources, empty-result behavior, and sibling routing, which is enough to call it correctly. The remaining gap is return-value structure, which the absent output schema leaves entirely to inference.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so both parameters are already documented, including the limit bounds and the hint that error messages belong to explain_error. The description restates "free-text description" and the error-message routing but adds no new syntax, length, or formatting guidance, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("Surface known UNRESOLVED problems") and enumerates the exact data sources: forum threads with zero replies but high views plus open GitHub issues. It also frames the user question it answers ("is anyone else hitting this?"), which lets an agent distinguish it from diagnostic siblings like explain_error or semantic_search without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly gives the when-to-use case, the when-not-to-use case ("Does NOT return fixes, solutions, config, or how-to steps"), and names the alternative path for the excluded case ("use semantic_search (then get_doc) instead"). It also notes the empty-result condition, so the agent knows a null return is not a failure.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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