GSA CALC+

price_reasonableness_check

Read-only

Evaluate a proposed hourly rate against GSA ceiling rate distribution.

GSA's keyword search matches labor titles, vendor names and contract
numbers. Check population_scope: unrelated titles can enter the pooled
statistics through a matching vendor or contract.

Returns MIXED_SEARCH_FIELDS with no verdict when off-title matches are
detected, or UNVERIFIED_POPULATION with no verdict when the title list
is missing, empty, truncated or approximate. Discover an exact title with
suggest_contains, then use exact_search on labor_category for title-only
rates. Filters match igce_benchmark, including sin and security_clearance.

Returns MIXED_LABOR_TITLES with no verdict when the phrase matches
different grades, specialties or title variants; pooled statistics remain
visible, with the exact-title count when verified. For example, Nurse I
must not be compared as if Nurse II/III/IV rates were the same grade.
Use exact_search with its supported education, experience and business-size
filters to inspect that title. It does not accept SIN or clearance filters;
check those requirements in the returned records and retain paging limits.

A verified exact-title population with at least 20 rates returns z-score,
median comparison, IQR position and delta from average. The z-score is
null when the mean or standard deviation is unavailable or variance is
zero. Missing means produce null average deltas.

A verified exact-title population with fewer than 20 rates returns LOW_SAMPLE
without a high/low verdict;
zero rates return NO_DATA. Statistics compare not-to-exceed ceiling
rates, not prices paid, and do not by themselves establish price
reasonableness or performance risk.

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": {
      "labor_category": "<labor_category>",
      "proposed_rate": "<proposed_rate>"
    },
    "name": "price_reasonableness_check"
  }
}

Input Schema

Name	Required	Description	Default
`sin`	No
`business_size`	No
`proposed_rate`	Yes
`experience_max`	No
`experience_min`	No
`labor_category`	Yes
`education_level`	No
`security_clearance`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 21, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Well beyond the readOnly/openWorld annotations: it names the failure codes (MIXED_SEARCH_FIELDS, UNVERIFIED_POPULATION, MIXED_LABOR_TITLES, LOW_SAMPLE, NO_DATA), states that each suppresses a verdict, gives the 20-rate threshold, and explains null z-score and null average-delta conditions. It closes with the crucial caveat that statistics describe not-to-exceed ceilings, not prices paid, and do not by themselves establish reasonableness.

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

Conciseness3/5

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

Front-loaded with the purpose, but the body is a dense block that interleaves this tool's error semantics, instructions for two sibling tools, and statistical caveats. Several clauses (e.g., the Nurse I/II example, the exact_search filter detour) could be tightened or moved.

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?

For an 8-parameter analytic tool it covers the decision-relevant behavior thoroughly: verdict suppression conditions, sample thresholds, and interpretive limits. With an output schema present it needn't spell out return shapes, and the annotation profile already covers safety, so the remaining gap is modest parameter-level detail.

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 0%, so the description must carry all parameter meaning. It documents sin and security_clearance as filters and implies labor_category must be an exact title, but education_level, experience_min/experience_max and business_size are only described as exact_search's filters, leaving their role in this tool unclear. It also discusses 'population_scope,' which is not an input parameter, adding some confusion.

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 object: 'Evaluate a proposed hourly rate against GSA ceiling rate distribution.' It also implicitly distinguishes itself from siblings by naming exact_search, suggest_contains and igce_benchmark as the tools that handle adjacent jobs.

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?

Explicit routing: use suggest_contains to discover an exact title, then exact_search for title-only rates; 'Filters match igce_benchmark, including sin and security_clearance.' It also warns that exact_search does not accept SIN or clearance filters, so the agent knows which tool to pick for which filter set.

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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