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Glama

Ca Procurement

by io.github.pipeworx-io

Ca Procurement Top Suppliers

ca_procurement_top_suppliers
Read-onlyIdempotent

Rank the biggest suppliers (vendors) to the State of California by total contract/purchase-order dollars, from the SCPRS purchase-order data on data.ca.gov. Answers "who are California's largest state contractors / award winners", optionally scoped to a department/agency, a fiscal year, an acquisition type, or an item keyword. Returns each supplier with total dollars awarded and purchase-order count, largest first. This is CALIFORNIA STATE data (not federal).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many top suppliers to return (default 20, max 100).
keywordNoOptional item name/description substring, e.g. "software", "consulting".
departmentNoOptional: restrict to one awarding department/agency (case-insensitive substring), e.g. "Health Care Services".
fiscal_yearNoOptional exact fiscal year, e.g. "2014-2015".
acquisition_typeNoOptional acquisition-type substring, e.g. "IT Goods", "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",
+    "limit": 20
+  },
+  {
+    "acquisition_type": "IT Services",
+    "fiscal_year": "2014-2015",
+    "limit": 10
+  }
+]
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=true, idempotentHint=true. The description adds valuable context: it returns suppliers with total dollars and order count sorted largest first, and clarifies the data is California state (not federal). No contradictions.

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: first sentence states core purpose, second adds optional scoping and output details. Every sentence adds value, no fluff. Front-loaded with the primary action.

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?

Despite no output schema, the description explains the output (supplier, total dollars, PO count, sorted). It also specifies the data source (SCPRS, data.ca.gov). The tool is simple and the description covers all necessary context for an agent to use it effectively.

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?

The input schema has 100% coverage, so baseline is 3. The description adds meaning by explaining parameters in context (e.g., 'case-insensitive substring' for department, default limit 20, max 100). This enhances understanding beyond the schema alone.

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 ranks the biggest suppliers to California by total contract dollars, using SCPRS data. It answers the specific question 'who are California's largest state contractors / award winners' and distinguishes itself from sibling tools like ca_procurement_supplier by indicating it returns a ranked list.

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 explicitly lists optional filters (department, fiscal year, acquisition type, keyword) and provides examples, indicating when to use them. However, it does not mention alternative tools or explicitly state when not to use this tool, which slightly reduces clarity.

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.

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