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Query Failure Graph

query_failure_graph

Query the Claidex failure graph for a specific gene target. Returns the Mechanism Risk Score (MRS), failure counts by phase and archetype, and all related claim slugs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
target_geneYesHGNC gene symbol, e.g. PIK3CA, KRAS, EGFR

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the burden. It discloses what the tool returns (MRS, failure counts, claim slugs), but it does not explicitly state that it is read-only or mention any side effects, rate limits, or error handling. The term 'query' hints at read-only behavior but is not explicitly stated.

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 concise: two sentences, first states the action and scope, second lists the outputs. There is no redundant information or filler. It is well-structured and front-loaded with the primary purpose.

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?

For a simple one-parameter query tool, the description provides the essential information: the resource, the parameter, and the return values. No output schema exists, so the description correctly explains the outputs. Minor omissions like prerequisites or limitations (e.g., whether the gene must already exist in the graph) prevent a perfect score.

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?

The schema covers 100% of the parameter with a clear description and examples (HGNC gene symbol). The description adds no new parameter semantics beyond the phrase 'specific gene target,' which merely parallels the parameter name. The baseline of 3 applies since the schema provides full coverage.

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 action ('Query') and the specific resource ('the Claidex failure graph') for a specific gene target. It also lists the return values (Mechanism Risk Score, failure counts, claim slugs), distinguishing it from sibling query tools like query_chembl or query_clinicaltrials.

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?

The description implies usage when a gene target is known and failure graph data is needed, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. No when-not-to-use guidance is given.

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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