GSA CALC+

igce_benchmark

Read-only

Get ceiling rate benchmarks for IGCE development.

Returns statistical summary for a labor category: count, min, max, avg,
median, standard deviation, percentile distribution (P10-P90), education
breakdown, worksite counts (worksite_breakdown: Customer_Facility /
Contractor_Facility / Virtual; counts only, prices are pooled across
sites), and outlier bounds.

GSA's keyword search matches labor_category, vendor_name and idv_piid.
The returned statistics pool all matching rate rows, including unrelated
labor titles when a vendor name or contract number matches the phrase.
Check population_scope and matched_titles before using the statistics.
off_title_matches_detected flags observed titles that do not match the
requested phrase; title_only_population_verified is false when the title
aggregation is missing, empty, truncated or approximate.
A title containing the phrase is not necessarily the exact grade:
'Registered Nurse I' also matches Nurse II, III and IV. Check
exact_title_population_verified, exact_title_rate_count and
non_exact_title_examples before treating a pool as that specific title.

For title-only rates, call suggest_contains on labor_category to discover
an exact title, then exact_search on labor_category. Different spellings
remain separate searches ('Cyber Security Analyst' vs 'Cybersecurity
Analyst'). Statistics are on current-year ceiling rates (current_price),
not next-year or option-year prices.

These are not-to-exceed ceiling rates, not prices paid. Ceiling-rate
comparisons alone do not establish a proposed price's reasonableness.

Optional filters: education_level, experience_min/max, business_size
('S' or 'O'), sin (e.g. '541611'), security_clearance ('yes' or 'no').

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>"
    },
    "name": "igce_benchmark"
  }
}

Input Schema

Name	Required	Description	Default
`sin`	No
`business_size`	No
`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

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnly/openWorld/non-destructive, yet the description adds substantial non-obvious behavior: matching pools across labor_category, vendor_name and idv_piid, off-title contamination, the population_scope / matched_titles / off_title_matches_detected flags, and that statistics use current_price (current-year ceiling) only. This is exactly the context an agent needs to avoid misreading results.

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?

Purpose and return shape are front-loaded, followed by matching caveats and then alternatives, then optional filters — a logical order for an agent deciding whether to call. Length is driven by genuinely load-bearing warnings (title-vs-grade ambiguity, pooled worksites) rather than filler.

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?

Given seven parameters, an output schema, and a statistically subtle result set, the description covers matching behavior, verification flags, price vintage, and interpretation limits. An agent has everything needed to call it correctly and to decide when the result is unusable.

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 0%, so the description carries the load, and it names every optional filter with enum values and an example ('sin (e.g. 541611)', business_size 'S' or 'O', security_clearance 'yes'/'no'). It also conveys the critical semantic caveat that labor_category is phrase/keyword matched rather than an exact lookup. Minor gap: experience_min/max units (years) and education_level accepted values are not specified.

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 ('Get ceiling rate benchmarks for IGCE development') and immediately enumerates what the response contains (count, min/max/avg, percentiles, education breakdown, worksite counts, outlier bounds). The domain-specific framing (IGCE, ceiling rates) separates it from siblings like filtered_browse or vendor_rate_card.

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?

Explicitly routes the agent: 'For title-only rates, call suggest_contains on labor_category to discover an exact title, then exact_search on labor_category.' It also sets boundaries on interpretation — 'Ceiling-rate comparisons alone do not establish a proposed price's reasonableness' — implying price_reasonableness_check for that purpose, and warns that different spellings are separate searches.

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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