get_projectGet full metadata for one PRIDE Archive proteomics project by accession (e.g. "PXD000001"): title, description, sample-processing and data-processing protocols, organisms, organism parts, diseases, instruments, keywords, and DOI.
| Name | Required | Description | Default |
|---|---|---|---|
| accession | Yes | PRIDE project accession, e.g. "PXD000001". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "accession": "PXD000001"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds the specific return contents (protocols, organisms, diseases, etc.), providing useful transparency about what the agent will receive. No contradictions with 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?
One well-structured sentence that leads with the core action and then enumerates the returned metadata fields. Every word adds value; no fluff.
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 getter with strong annotations, the description covers the return values adequately. It might optionally mention error handling (e.g., not-found behavior), but this isn't critical given the straightforward nature and good schema.
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 description coverage is 100%, so the schema already documents the accession parameter fully with an example. The description echoes the same example but adds no new semantic meaning beyond 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 specifies the exact action (get full metadata), the resource (one PRIDE Archive proteomics project), and the key qualifier (by accession). This distinguishes it from sibling tools like search_projects, which presumably search across projects.
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: use when you have a specific project accession and need complete metadata. It doesn't explicitly name alternatives or exclusions, but the accession-based identity makes the usage context unambiguous.
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.
ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, creating a true duplicate. The six-tool Polymarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) plus discover_tools vs suggest_questions give agents overlapping entry points that require deep reading to disambiguate.
Sub-families are internally consistent (ask_pipeworx_*, polymarket_*, remember/recall/forget), but the server mixes verb_noun, domain_noun, and bare-verb styles across tools. bet_research breaks the polymarket_ prefix pattern, and ai_visibility_check vs scan_competitor_ai_presence use different words for the same underlying concept.
33 tools is heavy and spans at least six unrelated domains: a data-gateway, prediction markets, key-value memory, subscription management, PRIDE proteomics, and standalone utilities (generate_llms_txt, scan_dependency). The scope is so broad that it feels like multiple servers merged into one, making the surface hard to navigate.
The dominant data-query domain is well covered: query, grounded query, deep research, profiles, comparison, change feeds, validation, entity resolution, and discovery are all present. Subscription and memory lifecycles are complete, and the prediction-market research surface is thorough; minor gaps exist only in peripheral areas like PRIDE project download/file details.