A
license-
qualityC
maintenanceSafety-first Model Context Protocol platform for AEC applications, enabling AI assistants to inspect, plan, validate, and modify engineering documents after policy and approval checks.
2
Apache 2.0
GroundTruth — subsurface scan QA & trade estimating
by ai.gtfi
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
- Server Listing
- GroundTruth MCP Server
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
MCP client
Glama
MCP server
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
100% free. Your data is private.
Tool Definition Quality
A4.2/5.0
Tool DescriptionsA
Average 4.2/5 across 3 of 3 tools scored.
Server CoherenceA
Disambiguation5/5
Each tool has a clearly distinct purpose: draft_estimate for generating bids, get_pricing for plan costs, and read_scan for analyzing subsurface scans. There is no overlap or ambiguity.
Naming Consistency5/5
All tools use lowercase snake_case with a consistent verb_noun pattern (draft_estimate, get_pricing, read_scan), making them predictable and easy to understand.
Tool Count4/5
With 3 tools covering two related domains (estimating and scanning), the count is slightly minimal but still appropriate for a focused service. Each tool is justified and not excessive.
Completeness4/5
The tools cover the core advertised features: pricing info, estimate generation, and scan analysis. Minor gaps like estimate management or scan history exist but do not hinder primary use cases.
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.
ParametersJSON Schema
| 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
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully informs behavior: it returns 'line items plus a link to brand it and send it to the customer' and mentions realistic US market prices. It does not specify destructive or auth-related behaviors, but those are not critical for a read-only estimate tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with the main action. Each sentence adds value: what it does, for whom, and what it returns. Could be slightly more concise, but no wasted words.
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 two simple parameters and no output schema, the description fully explains input format, behavior, output structure, and target users. No gaps identified.
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 the schema already documents both parameters. The description adds context about plain-English input and examples, but does not provide unique parameter-level details beyond the schema.
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 clearly states the verb ('turn into') and resource ('professional itemized bid'), and distinguishes from siblings like get_pricing and read_scan by specifying it creates a bid from plain-English input.
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?
The description specifies the target audience ('one-truck electrical, plumbing, HVAC and general-contracting shops') and use case ('contractor normally speaks this into their phone'), but does not explicitly state when not to use it or provide alternatives.
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
A4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full burden. It states 'Free to call' and what data is returned, but lacks details on side effects, authentication, or rate limits. For a read-only tool with no parameters, this is adequate but not thorough.
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?
The description is two sentences, front-loaded with the tool's purpose, and contains zero wasted words. Every sentence adds value.
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 a tool with no parameters and no output schema, the description is largely complete: it specifies the data returned and provides usage advice. Minor gaps exist regarding output format, but given the simplicity, it suffices.
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?
The input schema has no parameters (0), so schema description coverage is 100% by default. The description does not add parameter meaning, but none is needed. Baseline 3 is appropriate.
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 clearly states the tool retrieves 'Current GroundTruth and GruntPay plans and prices', uses a specific verb 'get' in the name, and distinguishes from sibling tools like 'draft_estimate' and 'read_scan' by focusing on pricing data.
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?
The description gives explicit usage guidance: 'use it before answering any cost question rather than guessing'. It does not explicitly mention when not to use, but the context is clear and no alternatives are suggested.
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.
ParametersJSON Schema
| 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
A4.4/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully explains the dual-model reading process, QA flag for disagreements, and that results are advisory. No contradictions exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the purpose. Every sentence adds value, though the first sentence is somewhat lengthy and could be slightly more structured.
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?
Despite no output schema or annotations, the description covers purpose, inputs, process, output highlights (targets, hazard class, QA flag), and limitations (advisory). Sufficient for a tool with 3 simple params.
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?
The input schema provides 100% coverage with descriptions for all three parameters. The tool description adds no further parameter details, so baseline of 3 is appropriate.
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 clearly states the tool provides an AI second opinion on subsurface scans, listing specific image types. It distinguishes itself from sibling tools like draft_estimate and get_pricing by focusing on scan analysis.
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?
It explicitly advises use 'before anyone cores or digs' and notes the advisory nature requiring technician sign-off. However, no explicit when-not-to-use or alternatives are provided, though siblings are unrelated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
- AlicenseAqualityCmaintenanceAI-powered terrain analysis for any location on Earth. Uses super-resolution and computes environmental risk metrics including erosion, water flow, and a composite Site Intelligence Score.3MIT
- Alicense-qualityCmaintenanceAI-powered access to OSDU subsurface data through natural language, enabling AI assistants to interact with the OSDU platform via conversational interfaces for search, schema management, records, and compliance.1Apache 2.0
- AlicenseAqualityCmaintenanceEnables AI assistants to interact with Autodesk Civil 3D through natural language, supporting tools for surfaces, alignments, profiles, corridors, pipe networks, COGO points, and AutoCAD geometry.91MIT
Try in Browser
Your Connectors
Sign in to create a connector for this server.