datasetsSearch the Bloomington Open Data catalog of open datasets by keyword. Returns each dataset's resource_id, name, description, category and update date — pass the resource_id to query/metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max datasets (1-100, default 20). | |
| query | No | Keyword to search dataset titles/descriptions (e.g. "budget", "crime", "health"). | |
| offset | No | Pagination offset. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "budget"
+ },
+ {
+ "limit": 10,
+ "offset": 0,
+ "query": "crime"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare safe operation (readOnlyHint, idempotentHint). Description adds value by listing return fields (resource_id, name, description, category, update date) and hinting at next steps.
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 sentences: first states purpose, second lists return fields and gives actionable instruction. No wasted words, front-loaded.
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 search tool with 3 well-documented parameters and no output schema, the description is sufficient. It covers purpose, return fields, and next steps. Slight lack of pagination detail but schema covers that.
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 clear parameter descriptions. Description does not add extra meaning beyond the schema, so baseline score of 3 is appropriate.
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 states verb 'Search', resource 'datasets', and context 'Bloomington Open Data catalog by keyword'. It distinguishes from sibling tools like 'query' or 'search_within' by specifying the catalog domain and return fields.
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?
Implies usage (search datasets then pass resource_id to query/metadata) but lacks explicit guidance on when to prefer this tool over siblings like 'search_within' or 'metadata'.
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.
Multiple tool clusters overlap heavily — ask_pipeworx_beta is functionally identical to ask_pipeworx right now, and the six Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research, etc.) have blurry boundaries. The entity-research family (entity_profar, compare_entities, recent_changes) is also easy to misselect despite detailed descriptions.
All names are snake_case, but conventions are mixed: verb_noun (resolve_entaty, validate_claim), bare verbs (remember, recall, query), adjective_noun (recent_alerts, recent_changes), and brand-prefixed nouns (polymarket_edges, pipeworx_trending). Family prefixes and verbs help readability, but the overall pattern is not uniform.
At 34 tools this exceeds the 25+ threshold and spans loosely related domains — Bloomington open data, Pipeworx research, prediction markets, AI marketing, memory, npm scanning, and llms.txt generation. The scope feels heavy and unfocused relative to the 'Data Bloomington' server name.
The core research lifecycle is well covered: routing (ask_pipeworx), grounded verification, deep research, entity profiles/comparisons/changes, claim validation, entity resolution, subscriptions, and memory all exist. Minor gaps remain — no standalone tool for fetching a pipeworx:// citation URI and no API-key management — but agents can work around them.