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Glama

search_legal_by_text

Free-text/semantic search over Indian-American immigration attorneys & law firms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNo
limitNo
queryYes
stateNo
offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / properties / offset
      Added value: +{
      +  "default": 0,
      +  "title": "Offset",
      +  "type": "integer"
      +}
  2. First observed

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It mentions 'free-text/semantic search' but does not specify whether matches are exact or ranked, whether the tool supports Boolean operators, or how results are ordered. Missing details on pagination behavior (e.g., max limit, offset semantics) and the nature of the output schema further reduce transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise (single sentence), which is beneficial, but it is too brief given the tool's complexity. It front-loads the purpose but omits essential details about parameters and behavior, making it under-specified for an agent's use.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 5 parameters (1 required), no parameter descriptions, no annotations, and an output schema that is not explained, the description is incomplete. It does not provide enough context for an agent to use the tool correctly, such as explaining how filters work together or what the search results contain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 5 parameters (query, city, state, limit, offset) with 0% schema description coverage. The description does not explain any parameter's meaning, defaults, constraints, or interactions. For example, the required 'query' parameter is not described, and optional filters like city/state lack format hints. The description fails entirely to compensate for the missing schema documentation.

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 states the tool performs 'Free-text/semantic search over Indian-American immigration attorneys & law firms,' which clearly defines the verb (search), resource (Indian-American immigration attorneys & law firms), and scope (free-text/semantic). This distinguishes it from sibling tools like get_legal_details (which retrieves details) or other search tools for different categories.

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 when to use this tool (when searching for attorneys/firms by text), but does not explicitly state when not to use it or mention alternative tools. It lacks guidance on scenarios where get_legal_details or other search tools might be more appropriate, leaving the agent to infer usage context.

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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