query_failure_graphQuery 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.
| Name | Required | Description | Default |
|---|---|---|---|
| target_gene | Yes | HGNC gene symbol, e.g. PIK3CA, KRAS, EGFR |
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.
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.
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.
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.
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.
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.
Add one secure layer between your agents and this server.
Several tools have overlapping responsibilities: search, search_claims, search_preprint_flags, and claidex_claim_risk_matrix all query claim/failure data, while rank_documents_by_embedding and rerank_documents both perform relevance ranking. The compatibility-oriented fetch/search tools add further confusion because their names collide with fetch_research_url and search_claims.
Names are grouped by prefixes (claidex_, query_, search_, run_) but the groups use different conventions, and bare verbs like 'fetch' and 'search' sit alongside prefixed forms like 'fetch_research_url' and 'search_claims'. The pattern is readable but not uniform.
24 tools is at the heavy end for an MCP server; while the breadth reflects many biomedical data sources and utilities, the count includes several meta/compatibility tools that could be consolidated. It is borderline but not unreasonable.
The surface covers the core biomedical workflows: searching claims, retrieving full claim content, querying failure graphs, checking preprints, and looking up drugs/trials/targets/adverse events. Minor gaps exist, such as no direct way to fetch a single clinical trial by ID beyond the search function, and no write/update operations for claims, but these are likely outside the read-only research scope.