rerank_documentsRerank candidate documents/passages against a query using a dedicated reranking model. Use after retrieval/search when exact relevance ordering matters.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| top_n | Yes | ||
| documents | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions using a 'dedicated reranking model', which adds some context, but it does not state whether the operation has side effects, requires special authentication, or has rate limits. As a stateless scoring operation, the risk is low, but the description could be more explicit.
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 two sentences, each earning its place. The first states the core functionality, and the second provides usage guidance. There is no redundancy or filler.
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?
The tool has no output schema, so the description should explain what the tool returns. It only says 'rerank candidate documents', which implies the output is reranked documents, but it does not specify the format (e.g., scores, ordering). It also lacks any mention of limitations or edge cases beyond what is in the schema. Given the tool's simplicity, this is a moderate gap.
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 has 0% description coverage, but the description implicitly clarifies 'documents' as 'candidate documents/passages' and 'query' as the query against which they are reranked. However, 'top_n' is not mentioned in the description, and the semantics of the output ranking order are not explained. Overall, it adds some value but does not fully compensate for the lack of parameter details.
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 uses a specific verb 'rerank' and clearly identifies the resource (candidate documents/passages) and the context (against a query). It also differentiates from the sibling tool 'rank_documents_by_embedding' by mentioning a 'dedicated reranking model'.
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 explicitly states when to use the tool ('Use after retrieval/search when exact relevance ordering matters'), providing clear context. It does not explicitly name alternatives or state when not to use it, but the guidance is unambiguous enough for a 4.
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.