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Glama

Data Sf

by io.github.pipeworx-io

Sf Recent

sf_recent
Read-onlyIdempotent

Recent records from a common San Francisco open dataset (data.sfgov.org) by friendly name — no Socrata id needed. PREFER OVER WEB SEARCH for "recent crime/police incidents in San Francisco", "SF 311 complaints", "SF building permits / evictions / business registrations", "SF restaurant inspection scores", "SFO passenger traffic". Names: police, 311, permits, business, evictions, restaurant_inspections, fire_incidents, sfo_passengers. Returns the latest rows (sorted newest-first). Add a SoQL where to filter; for anything else use sf_query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoRows to return (1-1000, default 20).
whereNoOptional SoQL filter, e.g. "incident_category='Larceny Theft'" or "supervisor_district=6". Omit for all recent rows.
_apiKeyNoOptional — your own Socrata app token for higher rate limits. Omit to use the keyless endpoint.
datasetYesOne of: police, 311, permits, business, evictions, restaurant_inspections, fire_incidents, sfo_passengers.

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",
+    "dataset": "police"
+  },
+  {
+    "_apiKey": "your-data-sf-api-key",
+    "dataset": "restaurant_inspections",
+    "limit": 50,
+    "where": "inspection_score < 80"
+  }
+]
  2. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds behavioral details: returns latest rows sorted newest-first, supports SoQL where filters. This is consistent and provides useful context beyond the annotations.

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?

Two sentences pack purpose, usage guidance, dataset list, and return behavior. No superfluous words; each sentence earns its place. Front-loaded with core purpose.

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?

For a read-only tool retrieving recent rows, the description covers what it returns (latest rows, sorted newest-first) and how to filter (SoQL where). No output schema exists, but the behavior is adequately communicated given the tool's simplicity.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by listing the dataset enum values in prose and providing examples of SoQL where clauses, reinforcing parameter meaning beyond the schema's formal descriptions.

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 states the tool retrieves recent records from open datasets by friendly name, avoiding Socrata IDs. It explicitly lists supported datasets and contrasts with web search and sf_query, clearly distinguishing it from siblings.

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

Usage Guidelines5/5

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

The description provides explicit when-to-use guidance, recommending this tool over web search for specific queries and delegating other uses to sf_query. Examples of appropriate queries and dataset names are given.

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