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Glama

Ca Procurement Commodities

ca_procurement_commodities
Read-onlyIdempotent

Rank what the State of California spends the most on, by commodity category (UNSPSC commodity title), from the SCPRS purchase-order data on data.ca.gov. Returns each category with total dollars and purchase-order count, largest first, optionally scoped to a department/agency or fiscal year. Answers "what does California buy the most of", "top spending categories for the CA Department of Health Care Services". This is CALIFORNIA STATE data (not federal).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many categories to return (default 20, max 100).
departmentNoOptional: restrict to one awarding department/agency (case-insensitive substring).
fiscal_yearNoOptional exact 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": "Health Care Services",
      +    "limit": 20
      +  },
      +  {
      +    "fiscal_year": "2014-2015",
      +    "limit": 15
      +  }
      +]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds useful behavioral details: returns total dollars and purchase-order count, sorted largest first, and limited to California state data. This goes beyond the annotations without contradiction.

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 concise (two sentences plus example queries) and front-loaded with the core purpose. Every sentence adds essential context; no wasted words.

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 no output schema, the description adequately explains the return structure (total dollars and purchase-order count) and data source. Annotations cover safety. It lacks mention of pagination, but the limit parameter and default max are in the schema. Overall complete for agent use.

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% with each parameter described. The description adds value by mentioning optional scoping by department/agency or fiscal year, and the example shows default limit and that department is a case-insensitive substring. This provides richer semantics than 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 explicitly states the tool ranks commodity categories by spending, using UNSPSC commodity titles, and gives example queries. It clearly distinguishes from sibling tools like ca_procurement_awards or ca_procurement_top_suppliers by focusing on categories rather than individual awards or 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?

The description provides clear context like 'Answers what does California buy the most of' and gives specific examples, making it easy for agents to decide when to use this tool. It does not explicitly list alternatives or when not to use, but the context is sufficient given the sibling tool names.

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

A3.7/5.0
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.