Searches local knowledge base semantically, returning relevant fragments with file paths and similarity scores to help locate information without using an LLM.
Busqueda semantica en la base local. Devuelve los fragmentos mas relevantes con su ruta de archivo y un score de similitud (0-1). No usa LLM.
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | ||
| query | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the return format (fragments with file path and similarity score 0-1) and the key behavioral trait of not using an LLM. However, it does not mention whether the operation is read-only or if there are side effects, though 'search' strongly implies read-only. It provides useful behavioral context beyond what the schema offers.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three short sentences with zero waste. The purpose is front-loaded, and the key differentiator ('No usa LLM') is placed at the end but still succinct. Every sentence earns its place; there is no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 params, one required) and the presence of an output schema, the description covers the core return format and the non-LLM aspect. However, it leaves the 'k' parameter unexplained, and with no annotations, it also fails to clarify that the operation is read-only. These gaps make the description only moderately complete for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate for undocumented parameters. It implies that 'query' is the search term but does not explicitly describe it, and it completely omits 'k' (which controls the number of results). The description adds some meaning to 'query' but fails to explain 'k', leaving a parameter undocumented in both schema and description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb (semantic search) and resource (local knowledge base), and differentiates from siblings by explicitly stating 'No usa LLM' (does not use LLM), which distinguishes it from ask_knowledge_base. The purpose is unambiguous and distinguishes from reindex as well.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool by noting it does not use an LLM, suggesting it is for direct retrieval rather than synthesized answers. It doesn't name the alternative explicitly, but the key differentiator is present, allowing an agent to infer the correct tool based on whether LLM-based processing is needed. This is clear context but not an explicit when-not statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.