Groundtruth: site inspections, photos and pickups; court and property records retrieval
Server Details
Site photos, inspections and pickups at an address; court, property and business records retrieved.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
- ChatGPT
- –
- Claude
- –
TDQS
Scored across 3 tools
Each tool maps to a distinct operation in a catalog pattern: listing services, describing one service, and submitting a requirement. The boundary between list_propositions and describe_proposition is clear, though the term 'proposition' versus 'request' in submit_request could momentarily blur the object being acted on.
All three names follow a consistent verb_noun snake_case pattern (list_, describe_, submit_). The only deviation is the noun: two tools operate on 'propositions' while submit_request switches to 'request', which is a minor inconsistency rather than a structural break.
Three tools is on the thin side but fits the discover-list, inspect-one, invoke pattern of a service catalog, and each tool clearly earns its place. No redundant or filler tools exist.
The discover/describe/submit cycle is covered, but once a requirement is submitted with a reference number there is no tool to check status, retrieve results, or list past requests. For a service mixing instant lookups with longer-running requirements, that tracking gap will likely cause dead ends.
Available Tools
3 toolsdescribe_propositionDescribe a serviceCRead-onlyIdempotentInspect
Everything about one service: what happens to a request, what you get, scope and limits, the request fields and any operations.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered by structured data. The description adds only that the payload is comprehensive in scope, without saying what happens for an unknown slug, whether an error is raised, or how large the response is. It neither contradicts nor meaningfully extends the annotations.
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?
It is a single front-loaded sentence with a colon-delimited list, which is structurally sound and short. But several list items are vague ('what you get', 'any operations'), so brevity comes at the cost of specificity rather than being purely 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?
There is no output schema, so the description is the only source of return-value information, and it does gesture at the main content categories. Against the simplicity of a one-parameter read tool with rich annotations, that is roughly adequate, but the undefined slug and absent error/edge-case behavior leave gaps.
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 single parameter, slug, has 0% schema description coverage, so the description carries the full burden and does not meet it — 'slug' is never explained as the service identifier, its expected format, or where to obtain it. The items listed ('request fields', 'operations') describe response content, not the input, so they do not compensate for the schema gap.
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 conveys that this is a detail-lookup for a single service and enumerates the kinds of content returned (request flow, response, scope/limits, fields, operations). However, it never states an explicit verb and shifts terminology from the tool name's 'proposition' to 'service', which leaves the agent to infer that these are the same entity. The singular framing loosely implies a contrast with list_propositions, but no explicit differentiation is made.
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?
There is no when-to-use guidance at all: nothing says to call this instead of list_propositions when the agent already has a slug, and nothing mentions the sibling tools. The description also never hints at what precondition must hold (a valid slug) before calling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_propositionsList Groundtruth's servicesARead-onlyIdempotentInspect
List Groundtruth's services with a one-line summary: lookups that run instantly, and requirements you can send.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered. The description adds only that each entry is summarized in one line and hints at two categories of service, but says nothing about ordering, volume, or whether the catalog changes over time.
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?
A single front-loaded sentence with no filler. The trailing clause ('lookups that run instantly, and requirements you can send') is somewhat cryptic but does convey that two kinds of services are listed.
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 parameterless, read-only listing with no output schema, the description gives enough to call it correctly and set expectations for a compact per-service summary. Only the shape and size of the returned list are left unstated, which is a minor gap.
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 tool takes no parameters, so the baseline is 4; there is nothing for the description to disambiguate. It correctly signals a parameterless catalog fetch rather than a filtered query.
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 names a specific verb and resource ('List Groundtruth's services') and states the presentation ('one-line summary'), so an agent knows it returns a compact catalog rather than details. It does not explicitly contrast itself with the sibling describe_proposition, which would have made the enumeration unambiguous.
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 phrasing implies a discovery/browse step before requesting detail, but there is no explicit when-to-use, when-not-to-use, or named alternative (e.g. 'call describe_proposition for full details'). Usage must be inferred from the sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
submit_requestSend a requirementCInspect
Send a requirement to one of Groundtruth's services. It is kept with a reference number.
| Name | Required | Description | Default |
|---|---|---|---|
| need | Yes | The requirement in words | |
| slug | Yes | Service slug, or anything-else | |
| action | No | ||
| budget | No | ||
| timing | No | ||
| contact | No | Optional; stored apart and deleted after 180 days | |
| quantity | No | ||
| geography | No | ||
| specifications | No | Fields from the service's request schema | |
| requested_fields | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare that the tool is non-readonly, non-destructive, non-idempotent and closed-world. The description adds one useful fact beyond them, namely that the submitted requirement is persisted with a reference number, which tells the agent that a write occurs and that an identifier comes back. However, it doesn't say that repeat calls create separate records, what the reference number looks like, or how the contact data is handled.
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 short sentences with no filler, and the destination and persistence fact are front-loaded. Brevity here is achieved by omission rather than by tight editing, but nothing is wasted.
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 ten-parameter, nested-submission tool with no output schema and thin annotations, the description is far too sparse. It omits the semantics of the optional structured fields and omits what happens after submission, so an agent can't reliably assemble a valid request from the description alone.
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?
Ten parameters, nested objects and only 40% schema description coverage mean the description should carry real weight, but it says nothing about need, slug, action, budget, timing, quantity, geography, specifications or requested_fields. With half the parameters undocumented even in the schema, the missing description leaves request construction largely guesswork.
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 states a concrete verb and resource: 'Send a requirement' to a named service. It is clear enough to distinguish from the read-only siblings describe_proposition and list_propositions, but it doesn't say what a 'requirement' actually is or how it differs from a proposition.
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?
There is no guidance on when to use this tool instead of the sibling tools, nor any prerequisites, such as needing a known slug or choosing 'anything-else'. The agent has to infer the usage context entirely.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
3 tool updates
- First observed
describe_proposition - First observed
list_propositions - First observed
submit_request
Related MCP Connectors
Cadastral investigation of people and companies: location, corporate ties/relationships, participati
OSINT investigations with traceable evidence: domains, URLs, IPs, scams and business records.
US public-records lookups: people search, reverse phone, property and criminal records.
NYC and US property records: owners, LLC officers, mortgages, pre-foreclosures and investors.
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