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Glama

Philadelphia Restoration

search_restoration_knowledge

Destructive

Semantic search across 60+ expert documents covering water and fire damage restoration. Topics include drying science, moisture mapping, equipment protocols, mold prevention (IICRC S520), fire restoration (IICRC S700), insurance adjuster tactics, contractor evaluation, and Philadelphia housing patterns. Returns relevant excerpts with source citations and relevance scores. Grounded in IICRC standards with section numbers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language search query (max 1000 characters)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.4/5.0
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description claims a read-only 'semantic search' that 'returns relevant excerpts', implying no side effects. Yet annotations declare destructiveHint=true and readOnlyHint=false, which directly contradict the described behavior. Annotation Contradiction flagged.

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 core action and resource, followed by useful details on topics, return format, and IICRC grounding. No redundant or filler content, making it efficient and well-structured.

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 single-parameter search tool with no output schema, the description adequately explains return values (relevant excerpts with citations and scores) and grounding. Missing explicit guidance about when to choose this over related tools, but that is addressed in the usage_guidelines dimension and is not critical for a search tool's basic functionality.

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 schema fully documents the single parameter 'query' with a description including max length and natural language. The tool description adds minimal additional meaning beyond identifying the query type, so with 100% schema coverage, the 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 a specific action ('semantic search') and a resource ('60+ expert documents covering water and fire damage restoration'). It distinguishes itself from sibling tools like assess_damage or get_emergency_steps by focusing on knowledge retrieval rather than direct assessment or action.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for expert restoration knowledge, listing relevant topics such as drying science, mold prevention, and insurance adjuster tactics. However, it does not explicitly state when to use this tool versus siblings like get_emergency_steps or assess_damage, nor does it mention any exclusions or alternatives.

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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