list_projectsList NCI Genomic Data Commons cancer-genomics projects (TCGA, TARGET, CPTAC, etc.), ranked by case count. Returns project_id, name, program, primary site(s) and disease type(s). Keyless, open-access metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| from | No | Offset into the result set for pagination (default 0). | |
| size | No | Number of projects to return (default 20, max 100). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "size": 20
+ },
+ {
+ "from": 0,
+ "size": 10
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds value by specifying that results are 'ranked by case count' and that it is 'open-access', which are behavioral traits not covered by structured 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?
The description is concise, consisting of two sentences with no extra words. It efficiently communicates purpose, context, and return fields, making it easy for an agent to parse.
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 simplicity (list with pagination) and the richness of annotations and schema, the description provides all necessary context: what the tool lists, what fields it returns, and its open-access nature. No further details are needed.
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 input schema already provides full coverage of the two parameters ('from' and 'size') with descriptions. The description does not add additional parameter-specific information beyond the schema, so it meets the baseline for high schema coverage.
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 action ('List'), the resource ('NCI Genomic Data Commons cancer-genomics projects'), and specific context (e.g., 'TCGA, TARGET, CPTAC') and ranking ('by case count'). It also lists the fields returned, making the tool's purpose unambiguous and distinct from siblings.
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 listing open-access projects but does not explicitly state when to use this tool versus alternatives like 'get_project' or 'search_cases'. The phrase 'keyless, open-access metadata' hints at no authentication requirements, but explicit guidance is lacking.
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.