get_projectLook up a single NCI GDC project by project_id (e.g. "TCGA-BRCA") and get its details plus summary counts (case_count, file_count). Keyless, open-access metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | Yes | GDC project id, e.g. "TCGA-BRCA", "TARGET-AML". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "project_id": "TCGA-BRCA"
+ },
+ {
+ "project_id": "TARGET-AML"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare idempotent, read-only, non-destructive. The description adds that it returns summary counts and notes 'keyless, open-access metadata,' which is useful behavioral context.
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 straightforward sentences, front-loaded with core action. No unnecessary words.
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 a simple lookup with one parameter, rich annotations, and no output schema, the description adequately explains return values (details plus summary counts) and access requirements.
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 a clear parameter description and examples. The description repeats the purpose but does not add new semantic detail beyond the 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 states the tool looks up a single NCI GDC project by project_id and returns details plus summary counts. It gives a concrete example (TCGA-BRCA) and distinguishes from list_projects by focusing on single project lookup.
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 implicitly indicates use for single project lookups but does not explicitly state when not to use or name alternatives like list_projects. However, the context of sibling tools makes it clear.
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.
Most tools have distinct purposes, but ask_pipeworx and ask_pipeworx_grounded overlap in routing (differ only in answer mode), and the multiple Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_kalshi_spread) could cause confusion without careful reading of descriptions.
Naming mix of verb-first (ask_pipeworx, compare_entities) and noun-first (entity_profile, recent_changes) patterns. Most use snake_case consistently, but the pattern is not uniform—some tools are commands, others are descriptors. Notable deviations like 'pipeworx_feedback' and 'pmc' are missing here but the sample shows inconsistency.
30 tools is on the high side, but the server covers a broad domain (data lookup, betting, AI, genomics, memory). Some tools could be merged (e.g., ask_pipeworx and its grounded variant), and the betting subdomain feels over-instrumented. The count is borderline between appropriate and heavy.
The server provides a wide range of operations for its diverse domains, but gaps exist: the genomics tools only offer basic metadata search (no download/analysis), and the betting tools lack historical data or backtesting. It covers common patterns but with notable omissions for a cohesive experience.