search_knowledge_baseAnswers from the app's own built-in knowledge base — 278 real support answers, extracted from the product. Measure Tech PRO is a full CRM for contractors; use this for how-do-I and does-it-do questions about measuring, drawings, the CRM, billing, and troubleshooting.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | What the person wants to know, in their words |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the tool's safety profile is known. The description adds value beyond this by disclosing that results come from a fixed set of 278 extracted support answers, and that the tool is not a general web search. This gives the agent useful context about the tool's behavior and limitations.
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 two sentences with no filler. The first sentence front-loads the primary behavior and source; the second adds relevant domain context and explicit usage guidance. Every sentence contributes to selection and invocation.
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 simple one-parameter search tool with readOnlyHint=true and no output schema, the description is largely complete: it states the source, the domain, and the types of questions to use it for. It would be stronger if it explicitly addressed when to prefer sibling tools like search_forum, but that is not a major 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 input schema fully covers the single 'query' parameter with a min/max length and description. The tool description does not add additional parameter-level semantics, which is acceptable because schema coverage is 100%. The baseline of 3 applies.
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 identifies a specific verb and resource: searching the app's built-in knowledge base of real support answers. It further defines the scope with explicit topic areas (measuring, drawings, CRM, billing, troubleshooting). It is clear but does not explicitly name or contrast with sibling tools like search_forum.
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 gives explicit use cases: 'how-do-I and does-it-do questions' across specific domains. This tells an agent when the tool is appropriate. However, it does not provide exclusion criteria or point to alternatives such as search_forum for community-based questions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools map to a clear resource/action, but get_product_overview and search_knowledge_base both claim to answer 'does it do X', and search_forum and get_roadmap both surface statuses, so an agent could pick the wrong one. The descriptions reduce the ambiguity but do not fully eliminate it.
The set uses lower_snake_case and broadly follows a verb_noun shape with get_/search_ prefixes for reads. However, my_requests and server_info are noun phrases and comment_on_request inserts a preposition, so the pattern is not perfectly uniform.
At 13 tools, the count is well within a reasonable range for the scope: product info, pricing, forum search/read/write, hearts/comments, waitlist, contact, and personal status. Each tool covers a distinct interaction area and none feels redundant enough to cut.
The surface covers the main customer journeys: learn about the product, search/read forum, post/comment/heart, check personal requests, ask for contact, and join the waitlist. Minor gaps exist (e.g., no edit/delete for comments or a way to check waitlist status), but agents can complete core workflows without dead ends.