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Glama

Ca Procurement

by io.github.pipeworx-io

Ca Procurement Supplier

ca_procurement_supplier
Read-onlyIdempotent

Aggregate a supplier's California STATE contract awards from the SCPRS purchase-order data on data.ca.gov: total dollars awarded, number of purchase orders, and a breakdown by awarding department (agency) and by fiscal year. Matches the supplier name as a case-insensitive substring, so it also surfaces name variants (e.g. "Pitney Bowes" vs "Pitney Bowes, Inc"). Use this to size up a single vendor's business with the State of California. This is CALIFORNIA STATE data (not federal).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
supplierYesSupplier / vendor name to aggregate (case-insensitive substring), e.g. "Deloitte", "Dell".
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: +[
+  {
+    "supplier": "Deloitte"
+  },
+  {
+    "fiscal_year": "2014-2015",
+    "supplier": "Dell"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds that matches are case-insensitive and surfaces name variants, and notes the data is California state, not federal. No contradictions. Provides useful context 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.

Conciseness5/5

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

Three concise sentences front-loaded with the main purpose. Second sentence adds behavioral detail, third gives usage guidance. No wasted words. Every sentence earns its place.

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?

The description explains the return structure (totals, counts, breakdowns) despite no output schema. With only 2 parameters and simple semantics, the description is complete for an agent to understand what the tool does and what it returns.

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 baseline is 3. The description reinforces 'case-insensitive substring' for supplier and optional fiscal_year, but does not add new meaning beyond the schema's own property descriptions. Adequate but not exceptional.

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 specifies the action (aggregate), resource (SCPRS California state contract awards), and outputs (total dollars, purchase orders, breakdown by department and fiscal year). It clearly distinguishes from sibling tools like ca_procurement_department or ca_procurement_top_suppliers by focusing on a single supplier.

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?

Explicitly states use case: 'size up a single vendor's business with the State of California.' Mentions substring matching for name variants. Does not explicitly list when not to use, but sibling tools provide context for alternatives. Clear enough for an agent to select appropriately.

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