GSA CALC+

suggest_contains

Read-only

Discover exact field values via autocomplete/contains matching.

Returns aggregation buckets showing matching values and their record
counts. Use this BEFORE exact_search() to find the right value string.

Minimum 2 characters required for the search term.

GSA lists at most 100 values, most records first. When more values
match, truncated is true and other_records counts the records under
values not shown; use a longer term to narrow the list.

Example workflow:
1. suggest_contains('vendor_name', 'booz') -> finds 'Booz Allen Hamilton Inc.'
2. exact_search('vendor_name', 'Booz Allen Hamilton Inc.') -> all their rates

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "field": "<field>",
      "term": "<term>"
    },
    "name": "suggest_contains"
  }
}

Input Schema

Name	Required	Description	Default
`term`	Yes
`field`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 21, 2026

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already cover the read-only, non-destructive, open-world profile, yet the description adds real operational context: a 100-value cap, most-records-first ordering, the truncated flag, and that other_records counts records under hidden values. It does not mention any latency, rate limits, or cost characteristics of the open-world lookup, but the truncation semantics are the important behavioral fact and they are clearly stated.

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?

Front-loaded with purpose, then return behavior, then usage rule, then edge-case semantics, then a compact example. Every sentence carries distinct information; the numbered workflow costs a few lines but removes ambiguity about pairing with exact_search.

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?

An output schema exists so return values needn't be spelled out, yet the description still explains the truncated/other_records fields that drive follow-up decisions. Combined with the min-length rule and the sibling-routing guidance, an agent has everything needed to invoke it correctly.

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 description coverage is 0%, so the description must carry the burden. It documents term well (minimum 2 characters, longer term narrows the result set) and both parameters appear in the worked example. The three enum values for field are self-describing in the schema, but the description never states that field is restricted to those three nor explains what each field contains, leaving a small gap.

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?

States a specific verb and resource ('Discover exact field values via autocomplete/contains matching') and immediately clarifies the output shape (aggregation buckets with matching values and record counts). It is clearly distinguishable from exact_search, which is named explicitly as the counterpart.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit ordering rule ('Use this BEFORE exact_search()'), a concrete two-step example workflow with real arguments, and a fallback tactic ('use a longer term to narrow the list') when results are truncated. No inference required about when to pick this tool over siblings.

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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