web_searchSearch the web for recent news, investor commentary, analyst notes, press releases, and conference coverage about drugs, clinical trials, or biomedical mechanisms.
| Name | Required | Description | Default |
|---|---|---|---|
| focus | Yes | any | |
| query | Yes | Search query |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits. It mentions 'recent' as a temporal filter but does not state that the tool is read-only, describe the return format (e.g., list of titles/snippets/URLs), or mention any rate limits or prerequisites. This minimal behavioral disclosure leaves the agent to infer most behavior from the tool name.
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 a single, front-loaded sentence that efficiently lists the tool's scope without redundancy. Every phrase earns its place, and it avoids filler or restating the tool name.
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?
Given the tool's low complexity (two parameters, no output schema, no nested objects), the description covers the core purpose and query scope. However, it lacks detail on expected return values or result structure, and without annotations, the agent is left without a complete picture of the tool's output. It is adequate but not fully comprehensive.
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?
Schema coverage is only 50% (query has a description, focus does not). The description compensates by clarifying the purpose of 'focus' implicitly—listing 'news, investor commentary, analyst notes, press releases' aligns with the enum values 'news' and 'investor', and the biomedical domain clarifies the 'query' content. While not explicit field-by-field, it adds meaningful semantic context beyond the bare schema.
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 identifies the tool's action ('Search the web') and its resource (web content), while specifying content types (news, investor commentary, analyst notes, press releases, conference coverage) and domain (drugs, clinical trials, biomedical mechanisms). This distinguishes it from sibling tools like search_pubmed (bibliographic database) and fetch (specific URL retrieval).
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 provides clear context for when to use this tool: when seeking recent web content about drugs, trials, or biomedical topics. It implies a recency requirement and enumerates content types, allowing the agent to match user intent. However, it does not explicitly name alternatives or exclusion cases, so it falls short of a 5.
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.