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casemgr-mcp

casemgr-mcp

by mstang
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

items-semantic_search

items-semantic_search

Search cases and items with natural language to discover conceptually related results, even without exact term matches.

Instructions

Semantic search across cases and items using natural language. Unlike items-search which matches by name only, this finds conceptually related items even when exact terms don't match. Requires embeddings to be enabled (EMBEDDINGS_ENABLED=true).

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": "items-semantic_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
entity_typesNoFilter by entity types: case, case_file_item. Default: all
limitNoMax results (default 20, max 50)
min_similarityNoMinimum cosine similarity threshold (0-1, default 0.3)
queryYesNatural language query to search for semantically similar items
typeNoFilter by item type (task, note, todo, bookmark, etc.)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations provided, so description carries full burden. It explains it finds conceptually related items using natural language, and that embeddings must be enabled. Does not detail return format or pagination, but adequately describes the tool's behavior.

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?

Two sentences: purpose and distinguishing info + prerequisite. No wasted words, front-loaded.

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?

Given complexity of semantic search and no output schema, the description covers the key behavioral context (embedding requirement, sibling comparison). Lacks mention of result format or scores, but still complete enough for an agent to understand usage.

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%; all parameters have clear descriptions. The description reinforces the natural language nature but adds little beyond the schema. Baseline 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?

Clearly states semantic search across cases and items. Distinguishes from sibling items-search by contrasting exact name matching vs conceptual matching.

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 compares to items-search and specifies the prerequisite: 'Requires embeddings to be enabled (EMBEDDINGS_ENABLED=true).'

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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