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jhammant
by jhammant

find_solutions

Search past conversations for error solutions, prioritizing results with fix or resolution language. Optionally filter by technology for targeted answers.

Instructions

Search past conversations for solutions to errors or problems. Prioritizes results containing fix/resolution language.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
technologyNoOptional technology context (e.g., 'docker', 'typescript', 'react')
error_or_problemYesThe error message, problem description, or issue to find solutions for
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It openly states that results are prioritized by fix/resolution language, which is a key behavioral trait. It also implicitly conveys a read-only operation through 'search'. It does not disclose return format or limitations, but the core seeking and ranking behavior is transparent enough for this simple search tool.

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 two sentences with no wasted words. It front-loads the main action ('Search past conversations') and adds a valuable ranking note in the second sentence. Every word earns its place.

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

Completeness3/5

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

The tool has no output schema, so the description should explain return values. It does not mention what the tool returns (e.g., snippets, conversation references, ranked list). It also does not clarify the scope of 'past conversations' (e.g., user-scoped vs. workspace-wide). This makes it incomplete for an agent that needs to consume the output.

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 coverage is 100%, and both parameters (error_or_problem and technology) are already described in the schema. The tool description adds no additional meaning beyond what the schema provides, so a baseline score 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?

The description clearly states the tool searches past conversations for solutions to errors or problems, using the specific verb 'search' and identifying the resource. It distinguishes itself from sibling tools like search_history by focusing specifically on solution-finding, and the additional note about prioritizing fix/resolution language further clarifies its unique purpose.

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

Usage Guidelines4/5

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

The description clearly implies when to use the tool: when you have an error or problem and need solutions from past conversations. However, it does not explicitly mention alternatives or exclusions, such as when to use search_history or find_patterns instead, which would elevate it to a 5.

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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