Kaleidoscope MCP (SEC & financial data)

Bio Entity Resolve (gene/target alias → canonical HGNC)

bio_entity_resolve

Read-onlyIdempotent

Superseded by resolve(identifier, kind='gene') (the shared resolver) — kept callable for one more release so existing prompts don't break, but new callers should use resolve instead. Resolve a user-facing biotech name (gene alias like 'PD-1' or 'HER2', modality, or indication) to the canonical identifier the rest of the bio tools expect. Returns ALL matching candidates — some queries are genuinely ambiguous (PD-1 → PDCD1 AND SNCA); the caller should pick. Resolves via an exact-normalize lookup against the curated HGNC gene gazetteer (sub-ms); on miss, falls back to semantic search over the targets collection so fuzzy queries like 'checkpoint inhibitor' still produce candidates. Use this first whenever a user passes a name to any other bio_* tool — the other tools want canonical symbols (PDCD1, ERBB2, ...).

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": "bio_entity_resolve"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes	Gene alias, modality, or indication name (e.g. 'PD-1', 'HER2', 'PSMA', 'GLP-1').
`k_fallback`	No	If the exact-normalize lookup returns nothing, how many semantic-search candidates to return (default: 5, max: 20).

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 10, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds substantive behavior beyond them: it returns ALL candidates rather than auto-picking, ambiguity is expected and the caller must disambiguate, resolution order is exact-normalize against the HGNC gazetteer (sub-ms) with a semantic-search fallback on miss. That fallback/ambiguity model is exactly what an agent needs and is not derivable from the annotations.

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?

Front-loads the most decision-relevant fact — the deprecation and its replacement — then the purpose, then the resolution mechanics. Every sentence carries routing or behavioral information, though the deprecation-plus-purpose framing takes three clauses before the core verb and could be tightened slightly.

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 2-parameter read-only resolver with no output schema, the description covers everything an agent would need: what it returns (multiple candidates), that ambiguity is normal, how it resolves, what happens on a miss, and whether to call it at all. Nothing material is left unstated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3; the description still adds meaning by framing `query` as spanning gene alias, modality, or indication, and by explaining the condition that triggers `k_fallback` ('if the exact-normalize lookup returns nothing'), which the schema states only as a count. It stops short of guidance on choosing k values, so not a 5.

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?

States a specific verb and resource ('Resolve a user-facing biotech name ... to the canonical identifier') with concrete input examples (PD-1, HER2) and the output class (HGNC symbols like PDCD1, ERBB2). It also explicitly names the sibling that replaces it, `resolve(identifier, kind='gene')`, so an agent can distinguish it without opening any schema.

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?

Gives an explicit when-to-use rule ('Use this first whenever a user passes a name to any other bio_* tool') and an explicit when-not/alternative rule (superseded by `resolve`, kept callable for one more release). Both the routing decision and the deprecation path are stated rather than inferred.

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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