ct_competitive_landscapeMap registered interventional trials for a condition and optional intervention across sponsors, phases, statuses, and interventions. The output reflects a bounded registry sample, not market share, scientific differentiation, or an exhaustive private pipeline.
| Name | Required | Description | Default |
|---|---|---|---|
| phase | No | ||
| status | No | ||
| condition | Yes | ||
| sample_size | No | ||
| intervention | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, and idempotent hints. The description adds crucial interpretive context: 'The output reflects a bounded registry sample, not market share, scientific differentiation, or an exhaustive private pipeline.' This clarifies the openWorldHint and helps agents avoid misinterpreting results.
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 well-structured sentences: the first front-loads the verb and purpose, the second provides a necessary caveat. No redundant information, direct and efficient.
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 5 parameters, no output schema, and 0% per-parameter coverage, the description is incomplete. It lacks explanation of sample_size, whether phase/status are filters, and the shape of the output. The caveat is valuable but does not compensate for these gaps.
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 has 5 parameters with 0% description coverage. The description explains condition (required) and intervention (optional), but leaves phase, status, and sample_size unexplained. The phrase 'across sponsors, phases, statuses, and interventions' may refer to output grouping rather than input filters, creating ambiguity for agents.
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 function: 'Map registered interventional trials for a condition and optional intervention across sponsors, phases, statuses, and interventions.' The verb 'map' and explicit resource/dimensions distinguish it from siblings like ct_count_by_condition or ct_search.
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 the tool: when you need a landscape overview of trials for a condition/intervention across sponsors, phases, statuses, and interventions. It does not explicitly name alternatives or exclusion criteria, but the focus on competitive landscape mapping implies its use case.
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 appear to do the same thing at the top level: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), and ask_pipeworx_grounded are near-duplicate routing entry points, and deep_research overlaps heavily with them. Among the ct_* tools, ct_count_by_condition, ct_competitive_landscape, ct_sponsor_pipeline, and ct_compare_sponsors all provide overlapping counting/landscape functionality, making correct selection genuinely ambiguous.
The overwhelming majority of tools use lowercase snake_case and mostly follow a verb_noun or domain-prefixed pattern (ct_search, ct_get_study, list_subscriptions, validate_claim, resolove_entity). Some names are noun phrases rather than verbs (ct_competitive_landscape, entity_profile, polymarket_edge_tracker) but the overall style is consistent and readable, with only minor deviations.
44 tools is far too many for a server named 'Clinicaltrials'; only 13 tools are actually clinical-trials-specific while the rest span general data lookup, prediction markets, memory, subscriptions, and npm scanning. The count is inflated by redundant entry points (ask_pipeworx/beta/grounded) and overlapping ct tools, making the set feel heavy and unfocused.
For the clinical-trials registry domain, the surface is largely complete: search, full study details, results summaries, condition counts, sponsor pipelines, location-based lookup, recent updates, and catalyst tracking are all represented. Minor gaps exist (e.g., historical versions/protocol amendments and advanced filter combinations), but most could be worked around via the universal ask_pipeworx router.