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Glama

Data Sf

by io.github.pipeworx-io

Sf Datasets

sf_datasets
Read-onlyIdempotent

Search the San Francisco open-data catalogue (data.sfgov.org) for datasets by keyword. Returns dataset names, descriptions, and Socrata resource ids to use with sf_query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax datasets (1-100, default 20).
queryNoKeyword(s), e.g. "parking", "housing", "tree".
offsetNoOffset for paging.
_apiKeyNoOptional — your own Socrata app token for higher rate limits. Omit to use the keyless endpoint.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-data-sf-api-key",
+    "query": "parking violations"
+  },
+  {
+    "_apiKey": "your-data-sf-api-key",
+    "limit": 30,
+    "query": "housing permits"
+  }
+]
  2. First observed

TDQS

A4.1/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering safety. The description adds limited behavioral context: it notes that _apiKey is optional and affects rate limits, and it describes the return values. No output schema exists, so the description partially compensates, but more detail on pagination or authentication would improve transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the tool's purpose, and contains no filler. Every word serves a purpose, making it highly concise and easily scannable by an AI agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there is no output schema, the description effectively conveys what the tool returns (dataset names, descriptions, and resource IDs) and how to use the results (with sf_query). Annotations cover safety and idempotency, so the description is complete for this search tool's requirements.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already describes all four parameters. The description adds marginal value by explaining '_apiKey' as optional for higher rate limits and 'query' as keywords, but these are mostly redundant with the schema descriptions. Baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches the San Francisco open-data catalogue by keyword and returns dataset names, descriptions, and resource IDs. It distinguishes itself from sibling tools like sf_query (which queries a specific dataset) and sf_recent, making its purpose specific and distinct.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool (searching for datasets by keyword) and explicitly mentions the returned IDs can be used with sf_query, guiding the agent on downstream usage. It does not explicitly state when not to use it, but the sibling tools offer alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0
Disambiguation3/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route questions to similar data sources, while search_within also overlaps with grounded answering. bet_research, polymarket_edge_tracker, and polymarket_fill_risk all target prediction markets. Agents must read descriptions carefully to pick the right variant.

Naming Consistency4/5

Most tools follow a verb_noun snake_case pattern (ask_pipeworx, bet_research, validate_claim, resolve_entity). Some are single nouns (recent_alerts, recent_changes, key_alerts), a few break the convention (ask_pipeworx_beta, ask_pipeworx_grounded, remember, forget). Overall mostly consistent with minor deviations.

Tool Count2/5

34 tools is a high count for a general-purpose data server, and several seem redundant: ask_pipeworx vs ask_pipeworx_beta vs ask_pipeworx_grounded, polymarket_edge_tracker vs polymarket_arbitrage, and the numerous meta-tools create overhead. A focused dataset server would be better with 10–15 tools.

Completeness4/5

The surface covers many domains well: SEC filings, economics/FRED, prediction markets, news, clinical trials, San Francisco open data, npm dependencies. Obvious gaps include no financial statement form filings beyond 8-K/10-K, no calendar/event scheduling, and no update-else path for several key objects (but memory tools fill that gap). Attribution currently ships in almost all requested tools, providing evidence.

Resources