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Ca Procurement

by io.github.pipeworx-io

Ca Procurement Awards

ca_procurement_awards
Read-onlyIdempotent

Search California STATE government contract awards & purchase orders from the official State Contract and Procurement Registration System (SCPRS) on data.ca.gov. Each result is a State purchase order / award with its awarding department (agency), the winning supplier (vendor), the dollar amount, acquisition type/method, item description, and dates. Filter by supplier, department/agency, keyword, fiscal year, acquisition type, and/or a minimum amount; results are ranked by dollar amount (biggest awards first) by default. Use this for questions like "who won California state contracts", "biggest suppliers to the CA Department of Health Care Services", or "CA state purchase orders for laptops". This is CALIFORNIA STATE data (not federal).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoSort order: "amount" = largest award first (default), "date" = most recent creation date first.
limitNoMax records to return (default 20, max 100).
offsetNoNumber of records to skip for pagination (default 0).
keywordNoCase-insensitive substring to match against the item name/description, e.g. "laptop", "consulting".
supplierNoWinning supplier / vendor name to match (case-insensitive substring), e.g. "Microsoft", "Pitney Bowes".
departmentNoAwarding State department / agency name (case-insensitive substring), e.g. "Health Care Services", "Transportation".
min_amountNoOnly return awards whose total price is at least this many dollars, e.g. 100000.
fiscal_yearNoExact state fiscal year, e.g. "2014-2015" (available: 2012-2013, 2013-2014, 2014-2015).
acquisition_typeNoAcquisition type substring, e.g. "IT Goods", "NON-IT Services", "IT Services".

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": "Health Care Services",
+    "fiscal_year": "2014-2015",
+    "keyword": "software",
+    "limit": 20,
+    "min_amount": 50000,
+    "supplier": "Microsoft"
+  },
+  {
+    "acquisition_type": "IT Goods",
+    "keyword": "laptop",
+    "limit": 10,
+    "sort": "date"
+  }
+]
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. The description adds context about ranking by dollar amount by default, max limit 100, and that it covers California state data only, enhancing transparency beyond annotations.

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

Conciseness4/5

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

Description is informative and front-loaded with the main purpose. Each sentence adds value, covering data source, result contents, filters, usage examples, and a disclaimer about scope. Slightly long but no redundancy.

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?

Given 9 parameters with no output schema, the description explains result fields (department, vendor, amount, etc.), default sorting, and provides filter examples. It covers pagination indirectly via schema and notes max limit. Lacks explicit mention of offset pagination but overall sufficient.

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% with detailed descriptions for each parameter. The description adds some extra context (e.g., case-insensitive substring, default sort), but does not significantly improve upon the schema's existing detail.

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 searches California state government contract awards and purchase orders, specifying the data source (SCPRS on data.ca.gov) and what each result contains (department, vendor, amount, etc.). It differentiates from siblings by focusing on awards, while siblings like ca_procurement_supplier target individual suppliers.

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?

Provides explicit example questions for when to use the tool, e.g., 'who won California state contracts'. Does not explicitly state when not to use or suggest alternatives, but the examples imply appropriate contexts.

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
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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