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search_solutions

Read-only

Use when: the user is blocked by a package, setup, runtime, config, or integration problem — search shared fixes before debugging from memory. Returns: matching solutions. When count > 0: next_action=record_agent_usage (required after the fix works) plus record_usage_solution_id. When count is 0: next_action, open_issues, and hint — if next_action is submit_open_issue, file it once you hit attempt churn (repeated failures / no progress), not immediately and not on token budget. Do not use when: browsing the open-issue queue (use search_open_issues) or posting new content. Safety: strip PII, secrets, internal paths, and proprietary project names from the query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoBrowse order when query is omitted (default: most_used). most_used = hot score (usage_count + addendum_count + recency); latest = newest first; random = random sample. Ignored when query is provided (semantic similarity is used instead).
limitNoMaximum number of results to return (default: 10, max: 50)
queryNoSearch query - try package names, error messages, or method names. Searches title, content, and model name. Strip any PII, file paths, internal hostnames, internal paths, proprietary project names, or secrets from the query before searching.
category_pathNoOptional ecosystem hint (e.g. 'python.requests', 'azure.container-apps', 'nodejs.express'). Lowercase dotted hierarchy; hyphens within segments. With a query, soft-boosts ranking — does NOT hard-filter. Without a query (browse), hard-filters to that path and children.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, and the description goes beyond that by disclosing conditional return behavior (count > 0 vs count = 0), the required next actions like record_agent_usage, and the safety requirement to strip PII, secrets, and internal paths from queries. No contradiction with annotations.

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

Conciseness5/5

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

The description is organized into clear labeled sections: Use when, Returns, Do not use when, Safety. Every sentence carries a distinct operational rule, and the most important usage trigger is front-loaded, making it easy for an agent to parse quickly.

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

Completeness5/5

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

For a tool with no output schema, the description covers the essential runtime context: when to call it, what it returns, how to handle both count outcomes, which sibling to use instead, and input sanitization requirements. Nothing needed for correct invocation appears missing.

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%, and the schema already thoroughly describes query, limit, sort, and category_path, including nuances like soft-boosting vs hard-filtering. The description adds little new parameter-level meaning beyond restating the PII-stripping rule already present in the query parameter's schema, so the baseline of 3 is appropriate.

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?

Description opens with a specific trigger ('user is blocked by a package, setup, runtime, config, or integration problem') and names the action ('search shared fixes'). It explicitly distinguishes itself from search_open_issues by saying browsing the open-issue queue should use that sibling instead, so an agent can select this tool correctly.

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?

Contains an explicit 'Use when' section and a 'Do not use when' section, naming search_open_issues as the alternative. It also gives conditional instructions for the count=0 path, including when to file an open issue based on attempt churn, which is precise decision guidance.

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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TDQS

A4.5/5.0
Disambiguation5/5

Each tool has a distinct purpose: adding addenda vs. editing core solutions vs. submitting new solutions vs. filing issues vs. searching, etc. There is no overlap; the descriptions clearly differentiate when to use each tool.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., add_addendum, search_solutions). The verbs are descriptive and the structure is uniform, making it predictable for an agent.

Tool Count5/5

With 9 tools, the server is well-scoped for a solution and issue management domain. Each tool serves a needed function without redundancy or bloat, covering the core workflows adequately.

Completeness4/5

The tool surface covers creation, reading, updating (via suggest_edit and add_addendum), and a reporting mechanism for problematic content. File submission and issue resolution are included, but there is no explicit tool for deletion or archival, which is a minor gap.

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