Skip to main content
Glama

Ca Procurement

by io.github.pipeworx-io

Ca Procurement Department

ca_procurement_department
Read-onlyIdempotent

Profile a California STATE department/agency's procurement spend from the SCPRS purchase-order data on data.ca.gov: total dollars, purchase-order count, top suppliers (vendors) it buys from, top commodity categories it spends on, and a by-fiscal-year breakdown. Matches the department name as a case-insensitive substring. Answers "what does the CA Department of Justice buy and from whom", "which agencies spend the most". This is CALIFORNIA STATE data (not federal).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
departmentYesState department/agency name (case-insensitive substring), e.g. "Justice", "Health Care Services", "Transportation".
fiscal_yearNoOptional: restrict to one fiscal year, e.g. "2014-2015".

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: +[
+  {
+    "department": "Justice"
+  },
+  {
+    "department": "Transportation",
+    "fiscal_year": "2014-2015"
+  }
+]
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, destructiveHint=false, etc., indicating safe read-only behavior. The description adds behavioral details: matches department name as case-insensitive substring, uses SCPRS purchase-order data from data.ca.gov, and clarifies it's state-level data. No contradiction with 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?

The description is two sentences plus an example usage, front-loaded with the core purpose. Every sentence adds value: first sentence states functionality, second adds matching method and data scope. No wasted words.

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

Completeness3/5

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

For a tool with 2 parameters and no output schema, the description is fairly complete: it specifies data source, matching method, and output categories. However, it does not mention pagination, limits, or error handling (e.g., department not found). With no output schema, some return format details would improve completeness.

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 description coverage is 100%, so the schema already describes both parameters. The description adds the detail that department matching is case-insensitive substring and provides an example fiscal_year format, but these are already in the schema descriptions. Minimal added value 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?

The description clearly states it profiles a California state department's procurement spend, listing specific outputs like total dollars, PO count, top suppliers, and commodity categories. It also gives example queries, distinguishing it from sibling tools like ca_procurement_top_suppliers or ca_procurement_awards.

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 explains what the tool does and provides example questions it answers. It clarifies the data scope (California state, not federal), which helps differentiate from federal-level tools. However, it does not explicitly state when to use this tool versus alternatives like ca_procurement_supplier or ca_procurement_commodities.

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

All Ca Procurement 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

A3.7/5.0
Disambiguation1/5

The tool set has severe overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve similar query/discovery purposes, and multiple polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research) overlap heavily in finding betting opportunities. An agent would struggle to select among these without deep familiarity, especially when ask_pipeworx and ask_pipeworx_beta are currently identical.

Naming Consistency2/5

Individual families are internally consistent (ca_procurement_*, polymarket_*, pipeworx_*), but the server as a whole mixes domain-prefixed snake_case, bare verb phrases (ask_pipeworx, bet_research), and descriptive noun phrases (entity_profile, recent_changes). More importantly, the vast majority of tool names have nothing to do with the server's stated 'Ca Procurement' identity, so the naming fails to signal a coherent tool set.

Tool Count2/5

36 tools is well past the 25+ threshold for 'too many,' and over 85% of them (31 tools) are unrelated to California procurement—they cover general data lookup, prediction markets, npm packages, and memory storage. A scoped CA procurement server would reasonably have 5–8 tools; this is a general-purpose data platform wearing a procurement label.

Completeness3/5

The five relevant ca_procurement_* tools cover the main read-side query patterns well: award search, commodity rankings, department profiles, supplier aggregation, and top suppliers. However, the surface lacks contract/award detail retrieval by ID, solicitation or RFP search, and any vendor registration or contract lifecycle data, leaving notable gaps for a procurement-focused tool set.

Resources