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search_nodes

Search typed project knowledge by hybrid full-text and vector matching, filtering by node type or status before fused ranking. Use it for project lookups instead of grepping graph files.

Instructions

Hybrid FTS+vector search, fused by Reciprocal Rank Fusion. types/status filter each branch's candidates before fusion (a filtered-out result never consumes a fusion rank slot it wasn't really competing within). Prefer this over grepping graph files for ANY lookup -- it matches paraphrase, filters by type/status, and returns typed nodes, which grep cannot.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_nodes"
  }
}

Input Schema

Name	Required	Description	Default
`k`	No
`query`	Yes
`types`	No
`status`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 18, 2026

TDQS

A3.7/5.0

Behavior4/5

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

With no annotations, the description carries the full load and does add real behavioral context: the hybrid FTS+vector mechanism, RRF fusion, and the fact that type/status filtering happens per-branch before fusion (so filtered results never consume a rank slot). It omits any note on result limits, pagination, or auth, but the algorithmic behavior is unusually well disclosed.

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?

Three sentences, mechanically front-loaded with the search model before the usage directive. The parenthetical about fusion rank slots is verbose but earns its place by explaining the filtering semantics.

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?

An output schema exists, so return values needn't be described. For a hybrid-retrieval tool the description covers mechanism, filtering behavior, and usage adequately; only 'k'/result-count behavior and the free-form status domain are left to the schema.

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 0% for 4 parameters, so the description must compensate. It explains how types/status filtering interacts with fusion, covering two of the four params, but never mentions 'k' or the status value domain, leaving half the parameters undocumented.

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

Purpose4/5

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

States a specific verb and mechanism ('Hybrid FTS+vector search, fused by Reciprocal Rank Fusion') and what it returns ('typed nodes'). It distinguishes itself from external grep but does not differentiate from close MCP siblings like find_similar or get_node, so an agent still has to infer the boundary.

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

Usage Guidelines3/5

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

'Prefer this over grepping graph files for ANY lookup' gives a clear when-to-use directive, but the alternative named is not an MCP sibling, and there is no guidance on when to choose find_similar, get_node, or get_subgraph instead. Usage is implied rather than fully scoped.

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