pharma_indication_landscapeBuild a disease-area landscape from registered interventional trials, FDA indication-label matches, and PubMed literature. Returns sponsor, phase, status, intervention, approved-label, and publication evidence for indication research.
| Name | Required | Description | Default |
|---|---|---|---|
| condition | Yes | Disease or condition. | |
| intervention | No | Optional drug, modality, or intervention filter. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable context on data sources (trials, FDA labels, PubMed) and the shape of returned evidence, enriching the behavioral model without contradicting annotations.
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?
Two focused sentences with no filler. The first sentence states the action and sources; the second lists outputs. All content earns its place.
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 2-parameter read-only research tool with no output schema, the description covers the main inputs, process, and return fields. It could mention pagination or result size limits, but the core use is clear. Annotations fill the remaining safety context, making this adequate.
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 100% with both parameters described ('Disease or condition.' and 'Optional drug, modality, or intervention filter.'). The description does not add extra parameter semantics beyond the schema, so the baseline of 3 applies.
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 tool's purpose: 'Build a disease-area landscape' with specific data sources (registered interventional trials, FDA indication-label matches, PubMed literature) and output fields (sponsor, phase, status, intervention, approved-label, publication evidence). This distinguishes it from sibling pharma tools like pharma_drug_profile or pharma_pipeline_scan.
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 for indication research and outlines what it aggregates, but it does not explicitly name alternative tools or exclusion criteria. Since the context is clear but no 'when not to use' is provided, it earns 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.
Every tool has a clearly distinct purpose, even within the same domain (e.g., ask_pipeworx vs ask_pipeworx_grounded vs ask_pipeworx_beta are differentiated by groundedness/beta status; polymarket_edges vs polymarket_arbitrage vs polymarket_fill_risk each target discovery vs arbitrage vs execution risk). The descriptions are highly detailed, eliminating ambiguity about when to use each.
All tool names use snake_case consistently, and most follow a verb-first pattern (ask_, compare_, discover_, search_, validate_), but a few are noun-first (entity_profile, polymarket_edges, recent_alerts). The style is readable and predictable, though not perfectly uniform in the verb_noun convention.
With 37 tools, the server is heavily over-scoped, especially given the 'Pharma Intel' name that suggests a focused pharma domain. Many tools are general-purpose (prediction markets, memory, subscription management, feedback) unrelated to the server's apparent purpose, making it feel like a grab bag rather than a cohesive set.
The pharma-specific tools cover drug profiles, safety, pipeline scans, catalysts, indication landscapes, and sponsor diligence – a solid lifecycle coverage. The broader data/query/prediction-market tools also feel complete for their respective sub-domains. The only minor gaps are niche operations (e.g., updating a subscription), but these are not critical to the core workflows.