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Glama

Data Sf

by io.github.pipeworx-io

Sf Query

sf_query
Read-onlyIdempotent

Run a raw SoQL query against any San Francisco open-data resource (data.sfgov.org) by its Socrata id (8-char like "wg3w-h783"). Full SoQL: where/select/group/order/limit/offset. Use sf_datasets to find a resource id, or sf_recent for the common ones.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
groupNoSoQL $group (e.g. "incident_category").
limitNoMax rows (default 100, max 5000).
orderNoSoQL $order (e.g. "incident_datetime DESC").
whereNoSoQL $where filter (e.g. "incident_year=2025").
offsetNoRow offset for paging.
selectNoSoQL $select (e.g. "incident_category, count(*)").
_apiKeyNoOptional — your own Socrata app token for higher rate limits. Omit to use the keyless endpoint.
resource_idYesSocrata resource id, e.g. "wg3w-h783" (police incidents).

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",
+    "limit": 100,
+    "resource_id": "wg3w-h783",
+    "where": "incident_year=2025"
+  },
+  {
+    "_apiKey": "your-data-sf-api-key",
+    "group": "incident_category",
+    "order": "count(*) DESC",
+    "resource_id": "wg3w-h783",
+    "select": "incident_category, count(*)"
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint. Description adds value by mentioning rate limits and option of app token for higher limits, and implies no destructive behavior.

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 concise sentences front-loading the core purpose and usage, with no wasted words. Examples in schema provide further clarity.

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

Completeness4/5

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

Covers how to find resource IDs, SoQL capabilities, and authentication. No output schema, but description adequately sets expectations for a query tool. Slight gap on return format.

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 has 100% description coverage with clear explanations. Description adds context about SoQL clauses but does not significantly enhance parameter meaning beyond the schema.

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?

Clearly states it runs raw SoQL queries against San Francisco open-data using a Socrata ID. Distinct from sibling tools like sf_datasets (finds IDs) and sf_recent (common datasets).

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?

Explicitly mentions using sf_datasets to find resource IDs and sf_recent for common ones, guiding when to use alternatives. Could include when not to use, but sufficient for context.

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