GroundTruth — subsurface scan QA & trade estimating
Server Details
Two independent AI models read a GPR, locate or pipe scan and flag the do-not-core call.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- Dranos-Bot/groundtruth-mcp
- GitHub Stars
- 0
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Tool Definition Quality
Average 4.4/5 across 3 of 3 tools scored.
Each tool has a clearly distinct purpose: one for drafting estimates, one for retrieving pricing plans, and one for analyzing subsurface scans. There is no overlap or ambiguity.
All tool names follow a consistent verb_noun pattern (draft_estimate, get_pricing, read_scan) with snake_case, making them predictable and easy to interpret.
With only 3 tools covering two distinct domains (subsurface scan QA and trade estimating), the count feels slightly minimal but is still reasonable for a focused server. It sits at the lower end of the typical range.
The server lacks basic management operations like listing or deleting estimates or scans. Users cannot retrieve past estimates or manage scan records, leading to potential agent failures when non-CRUD workflows are needed.
Available Tools
3 toolsdraft_estimateAInspect
Turn a plain-English description of a trade job into a professional itemized bid — real materials by size, fittings, consumables and labor on its own line, at realistic US market prices. Built for one-truck electrical, plumbing, HVAC and general-contracting shops. This is GruntPay: the contractor normally speaks this into their phone. Returns the line items plus a link to brand it and send it to the customer.
| Name | Required | Description | Default |
|---|---|---|---|
| trade | No | electrical, plumbing, hvac, or general. Defaults to general. | |
| job_description | Yes | What the contractor did or will do, in plain words. e.g. "Replaced the panel, 85 feet of conduit, 8 can lights, 16 hours." |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool generates itemized lines for materials, fittings, consumables, and labor at US market prices, and returns a link for branding and sending. This transparently communicates the tool's behavior and output without concealing any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences long, each serving a distinct purpose: the first defines the core functionality, the second specifies the audience, and the third adds operational context (voice input, output with link). No unnecessary words, and the key action is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool that generates estimates from a plain description, the description covers key aspects: inputs, output structure (line items plus link), and pricing assumption. However, it could also mention any limits (e.g., length of job_description) or the exact output format (e.g., JSON structure). Nonetheless, it is adequate given the schema coverage and typical use case.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes both parameters with 100% coverage. The description adds significant value by providing examples ('Replaced the panel...') for 'job_description' and clarifying that 'trade' defaults to 'general', making the parameter usage intuitive and reducing ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it turns a plain-English job description into an itemized bid with real materials and labor. It names specific trades (electrical, plumbing, HVAC, general) and distinguishes from sibling tools like 'get_pricing' and 'read_scan' which likely serve different purposes, such as retrieving prices or scanning documents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description identifies the target users (one-truck shops) and use case (contractor speaking into phone). It implies this tool is for creating bids from verbal descriptions but does not explicitly state when not to use it or mention alternatives like 'get_pricing' for pricing lookup. Still, context is clear enough for appropriate selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_pricingAInspect
Current GroundTruth and GruntPay plans and prices. Free to call — use it before answering any cost question rather than guessing.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description states 'Free to call', hinting at no cost/side effects, but no explicit read-only or idempotency guarantee. With no annotations, description carries burden but lacks full disclosure (e.g., no mention of rate limits or caching).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, zero superfluous content. Front-loaded with core action ('Current...plans and prices') and usage instruction. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool is simple parameterless retrieval. Description covers purpose and return content (plans and prices). No output schema, but content is self-evident. Siblings confirm distinct context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters in schema (schema coverage 100%), so description naturally adds no param info. Baseline 4 per rubric for 0 parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description specifies retrieving 'GroundTruth and GruntPay plans and prices', a clear verb+resource. Distinguishes from siblings 'draft_estimate' (estimation) and 'read_scan' (scan reading) which are unrelated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs to use 'before answering any cost question rather than guessing', providing clear when-to-use. Does not list when-not-to-use or alternatives, but context implies exclusive use for cost queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_scanAInspect
Get an independent AI second opinion on a subsurface scan before anyone cores or digs. Send a public image URL of a GPR scan, a marked-up concrete slab photo, a utility-locate markup, a concrete radiograph, or a CCTV pipe frame. TWO different AI models read it independently — the second is prompted to refute the first — and you get back every located target with its hazard class (do-not-core / rebar / verify / clear), plus a QA flag when the two reads disagree. Advisory only: a certified technician must review and sign off before any cutting.
| Name | Required | Description | Default |
|---|---|---|---|
| image_url | Yes | Public https URL to a JPEG or PNG of the scan or the marked-up surface. Max 8 MB. | |
| discipline | No | One of: gpr (concrete scanning), locate (utility locating), pipe (CCTV sewer), xray (concrete radiograph), milestone (FL SB 4-D condo concrete). Defaults to gpr. | |
| field_note | No | Optional: what the technician said on site. Used as context for names and depths only — it can never clear a hazard the image shows. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the burden. It discloses that two AI models work independently, the second refutes the first, and outputs include hazard classes and a QA flag for disagreements. It also clarifies the advisory nature and limitation that field_note cannot clear hazards.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the main purpose, but it consists of a single long sentence that could be broken for improved readability. It efficiently uses words without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 value (hazard classes and QA flag). It covers the tool's behavior and inputs well, though it could mention potential error cases or validation steps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters are well-documented in the schema. The description adds context beyond the schema, such as the purpose of field_note and the default for discipline, which enhances understanding of how parameters are used.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'an independent AI second opinion on a subsurface scan'. It lists acceptable input types and output details, making the purpose specific and distinguishing it from sibling tools like draft_estimate and get_pricing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies when to use the tool: 'before anyone cores or digs'. It also notes that it is advisory only and requires technician sign-off. However, it does not explicitly contrast with sibling tools or state when not to use it, though the differentiation is implied.
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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