Ahrom Labs
Server Details
Indian ERP checklist, IBR 2026 compliance, BIS BOQ standards, and engineering notes.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
TDQS
Scored across 1 tool
With only one tool there is no possibility of confusing it with a sibling, so selection errors are structurally impossible. However, the description 'Finds exactly what you're looking for' is pure boilerplate and gives an agent no signal about what is being searched, so the tool's actual purpose remains ambiguous.
A single bare verb ('search') is internally consistent but establishes no discernible pattern, and it lacks the object noun a verb_noun convention would provide. There is nothing inconsistent here, but also nothing predictive an agent could rely on.
One tool is too thin for a server that presents itself as a general-purpose 'Labs' offering; there is no stated scope that this single operation could plausibly cover. A lone generic 'search' leaves the surface effectively un-scoped.
The domain cannot even be inferred from the name or description, and a single search op has no companion operations (no fetch-by-id, no list, no create/update). Any real workflow would hit a dead end immediately.
Available Tools
1 toolsearchDInspect
Finds exactly what you're looking for
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The input query. | |
| ai_search_options | No | Options for customizing the AI search behavior. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden and discloses nothing: not that this is a read-only retrieval operation, not that results are capped at 50, not that cache/reranking/query-rewrite toggles alter behavior, not what is returned. For a search endpoint with a deeply nested options object, this is a near-total absence of behavioral context.
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 short, front-loaded sentence with no padding, so it is concise. However, brevity here is achieved by omitting content rather than by compressing it, leaving the sentence effectively information-free.
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?
The tool takes a nested ai_search_options object with caching, reranking, query rewriting, metadata filters, fusion methods, and result caps, yet the description explains none of it. Given no annotations and no output schema, the definition is far too thin for an agent to invoke this correctly.
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 description coverage is reported at 100%, so the baseline is 3 even though the description adds nothing about the query or the ai_search_options object. Notably the nested retrieval fields (retrieval_type, fusion_method, match_threshold, context_expansion) carry no descriptions at all, so the schema is doing only the minimum here.
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 says only that it "Finds exactly what you're looking for" – essentially a restatement of the name "search" dressed as marketing copy. It never states what corpus or data domain is being searched, what kind of matches are returned, or any scope boundary that would let an agent distinguish this tool from a generic lookup.
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: no prerequisites, no conditions, and no alternatives named (the sibling list is empty, so there is nothing to compare against, but also no context about the intended call context). The agent is left to infer entirely when this tool is appropriate.
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.
1 tool update
- First observed
search
Related MCP Connectors
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AI reasoning checks any document against known international standards before your agent acts on it.
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