Skip to main content
Glama

Data Cambridge

by io.github.pipeworx-io

Cambridge Recent

cambridge_recent
Read-onlyIdempotent

Recent records from a common Cambridge open dataset (data.cambridgema.gov) by friendly name — no Socrata id needed. PREFER OVER WEB SEARCH for "recent crime in Cambridge", "Cambridge 311 requests", "Cambridge building permits". Names: 311, crime, permits. Returns the latest rows (newest-first). Add a SoQL where to filter; for anything else use cambridge_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: 311, crime, permits.

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already mark readOnly, idempotent, nondestructive. Description adds ordering ('newest-first') and that a where clause can filter, supplementing the annotation coverage well.

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?

Compact but information-dense: usage guidance, dataset list, ordering, filtering advice, and alternative in few sentences, front-loaded.

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 the tool's simplicity (no output schema, simple params), the description fully covers use cases, datasets, filtering, ordering, and alternative tools.

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% (baseline 3). Description adds concrete dataset names and explains the where parameter's purpose, improving clarity beyond 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?

The description clearly states the tool retrieves recent records from Cambridge open datasets by friendly name, lists the three datasets, and distinguishes from sibling tools like cambridge_query and web search.

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?

Explicitly recommends use over web search for specific queries, says to use cambridge_query for anything else, and mentions adding SoQL where clauses for filtering.

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

All Data Cambridge toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation3/5

Most clusters have clear roles, and the detailed routing guidance separates ask_pipeworx from deep_research and grounded mode. However, three ask_pipeworx variants (one currently identical to stable), plus overlapping opportunity-discovery tools (polymarket_edges vs bet_research) and discovery/onboarding tools (discover_tools vs suggest_questions), leave several boundary cases where an agent could select the wrong tool.

Naming Consistency3/5

Many tools group under readable prefixes (ask_pipeworx, cambridge_, polymarket_, pipeworx_) and are mostly snake_case. But the set mixes bare verbs (remember, recall, forget), verb_noun actions (resolve_entity, validate_claim), and noun-phrase names (entity_profile, recent_changes, polymarket_fill_risk), so no single convention predicts the full API.

Tool Count2/5

34 tools is well into the 'too many' band, and the count is inflated by a kitchen-sink mix of data querying, prediction-market analysis, memory, subscriptions, feedback, llms.txt generation, and npm scanning. The server name 'Data Cambridge' suggests a narrow local-data scope, which makes the sprawl look even less appropriate.

Completeness3/5

Individual verticals are fairly complete: query/grounded/beta router levels, entity resolution/profile/compare/change, prediction-market discovery through fill-risk, and full memory and subscription CRUD. The major gap is discover_tools, which returns tools promoted as ready to call directly but no generic invocation tool is exposed, so the agent must route back through ask_pipeworx.

Resources