searchSearch Claidex failure claims and preprint flags. This compatibility tool returns citation-ready results for OpenAI and ChatGPT remote MCP flows.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | Yes | ||
| query | Yes | Biomedical failure intelligence query. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure. It mentions returning 'citation-ready results' but does not explain output format, pagination, limitations, or read-only status.
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 and front-loaded with the core purpose. The second sentence adds context, though 'compatibility tool' is vague and slightly detracts from clarity.
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?
There is no output schema, so the description should at least clarify the shape and nature of results beyond 'citation-ready'. Given the large sibling tool set, the description lacks sufficient context for an agent to reliably choose and invoke this tool.
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 provides a description for 'query' and constraints for 'limit', but the description adds no parameter-level meaning. With only 50% schema coverage, the description does not compensate for the lack of detail on 'limit' or query formatting.
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 it searches Claidex failure claims and preprint flags, with a specific verb and resource. However, the phrase 'compatibility tool' is ambiguous and sibling tools like search_claims and search_preprint_flags overlap, so differentiation is weak.
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?
No explicit guidance is given on when to use this tool versus alternatives such as search_claims or search_preprint_flags. The reference to OpenAI and ChatGPT remote MCP flows hints at a niche use case but does not state exclusions or selection criteria.
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.