get_studyGet full details for one cBioPortal cancer study by its study id (e.g. "brca_tcga_pub", "glioma_mskcc_2019"), including description, cancer type, sample count, PMID, and citation.
| Name | Required | Description | Default |
|---|---|---|---|
| study_id | Yes | cBioPortal study id, e.g. "brca_tcga_pub". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, etc. Description is consistent and adds context about returned fields (description, cancer type, etc.), but no new behavioral traits beyond 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?
Single sentence, front-loaded, concise, and includes all necessary information. No wasted 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?
For a simple one-parameter read tool with full annotations and no output schema, the description fully covers what the tool does, what input is needed, and what output to expect.
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% and already describes study_id well. Description adds no parameter-specific details beyond the schema example, meeting 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?
Description clearly states the tool retrieves full details for a cBioPortal cancer study by study ID, with examples. It distinguishes from siblings like search_studies and get_gene by specifying the exact resource and action.
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?
Implicitly clear when to use (have a specific study ID, need full details), but no explicit when-not-to-use or alternatives. Sibling search_studies exists for searching, but not mentioned.
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 clearly distinct purposes, but ask_pipeworx_beta is an intentional near-duplicate of ask_pipeworx, and several polymarket/entity tools overlap in scope. The descriptions do enough to disambiguate most pairs, but the duplicate beta routing tool introduces real ambiguity.
All names use snake_case, but the pattern varies: verb_noun (get_gene, search_studies), noun_noun (polymarket_edges, pipeworx_trending), and product-prefixed verbs (ask_pipeworx, bet_research). There is no single consistent convention, though the names remain readable.
35 tools is a heavy surface, and the vast majority (31) are unrelated to cBioPortal; only four tools actually belong to the named domain. This makes the count inappropriate for a cancer-genomics MCP server, as the set is bloated with out-of-scope utilities.
For a cBioPortal server, only metadata-level tools exist (gene lookup, study details, cancer types, study search); core cBioPortal data access — mutations, copy-number alterations, clinical data, molecular profiles, sample-level queries — is entirely missing. The tool surface severely under-covers the named domain.