Mini Agent MCP

by Microbiosis

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Server Quality Checklist

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Latest release: v1.0.1
Disambiguation5/5
Each tool has a distinct purpose: search tools (anysearch_search, anysearch_batch_search, anysearch_extract, anysearch_get_sub_domains) are clearly separated, and utility tools (calculator, datetime_info, etc.) are unique. No two tools overlap in functionality.
Naming Consistency4/5
Tool names follow a pattern: Anysearch tools are prefixed with 'anysearch_', while built-in utilities use plain names like 'calculator'. This split is consistent within categories but introduces two naming conventions, which is a minor deviation.
Tool Count5/5
With 11 tools covering search, extraction, domain discovery, and common utilities (calculator, datetime, random, text operations, unit conversion), the count is well-scoped for a general-purpose assistant with web search capabilities.
Completeness4/5
The tool surface covers core operations: search (including batch and extraction), domain routing, and basic utilities. Minor gaps like file handling or more advanced text processing are absent, but the set is sufficiently complete for its stated purpose.
Average 4.4/5 across 11 of 11 tools scored. Lowest: 3.6/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 70 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
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If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavioral traits. It states that the tool returns statistics, implying a read-only operation, but does not explicitly confirm it is non-destructive or side-effect-free. Lacks details on limitations or required permissions.
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 a single, well-structured sentence that front-loads the action and immediately lists outputs. Every word earns its place with no redundancy or filler.
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?
Given the tool's simplicity (one parameter, no output schema, no annotations), the description is reasonably complete. It explains inputs and outputs, though it could mention potential constraints (e.g., text length limits) to be fully self-contained.
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 input schema has 100% description coverage for the single parameter 'text', so the description does not need to add parameter details. The description adds context on what statistics are computed, which indirectly helps understand the parameter's role, but does not surpass a baseline of 3.
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 the tool's action ('Analyze text') and resource ('text'), and enumerates specific outputs (character count, word count, etc.), making it easy to understand what the tool does. No sibling tool duplicates this functionality, so differentiation is clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 versus alternatives like 'text_transform' or when not to use it. The description does not provide context about prerequisites, preferred scenarios, or exclusions, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully convey behavior. It explains step-by-step reasoning and two execution modes, but does not disclose potential side effects (e.g., network calls if anysearch tools are used) or latency considerations. The behavioral description is adequate but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with paragraphs, bullet-point examples, and tool categories. It is front-loaded with the main purpose. While slightly long, every part adds context. Could be slightly more concise but is effectively organized.
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?
Given the tool's complexity as an orchestrator, the description covers key aspects: purpose, reasoning process, execution modes, and dependencies (LLM config). It lists available tools. Missing details like error handling or limitations, but overall sufficient for an agent to understand usage.
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?
Schema coverage is 100% for the single 'task' parameter, so baseline is 3. The description adds value through examples but does not elaborate on task format or constraints beyond what the schema says ('Be specific'). The added semantics are marginal.
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 explicitly states the tool runs a mini ReAct agent that autonomously uses all available tools. It provides a clear verb-resource pair and distinguishes itself from sibling tools (which are individual tools, while this is an orchestrator). Examples further clarify its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lists example tasks and explains when to use the tool (multi-step tasks requiring reasoning). It also notes conditions for LLM vs rule-based mode. However, it does not explicitly state when not to use it or compare to alternatives beyond listing sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavioral traits: it explains each operation's process (e.g., 'number' generates random int in range, 'uuid' generates UUID v4) and defaults (password length 16, etc.). No destructive behavior is mentioned, which is consistent with a read-only generator.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence efficiently listing operations and their parameters. It is front-loaded with 'Generate random values.' While dense, it covers all necessary information without waste. A slightly more structured format would improve readability.
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?
Given the tool's complexity (5 operations, 8 parameters) and no output schema, the description provides sufficient context about each operation's inputs and behavior. It does not cover return values, but that is acceptable per guidelines. It is complete for a stateless random value generator.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds meaning by grouping operations and clarifying required parameters per operation (e.g., 'min' and 'max' for number, 'items' for pick/shuffle), which is not evident from the schema alone.
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 'Generate random values' and enumerates five specific operations (number, uuid, password, pick, shuffle), providing a clear verb and resource. The tool is distinctly different from siblings like search, calculator, and text tools, so no sibling confusion.
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 implicitly tells when to use each operation via parameter requirements, but lacks explicit guidance on when to use this tool vs alternatives or when not to use it. Since siblings are unrelated, the context is adequate but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It lists operations and param requirements but does not disclose whether operations are read-only or have side effects. However, the nature of text transformation is inherently non-destructive.
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 a single sentence with a clear bullet-like list. It is front-loaded with the purpose and each word adds value.
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?
Given no output schema and 4 parameters, the description covers operations and param dependencies. It does not explain return values, but for a text transform the output is obvious.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by specifying which parameters are needed for which operations, e.g., 'count_substring (requires 'pattern' param)'.
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 the tool transforms text in various ways and lists all specific operations. It distinguishes itself from sibling tools which are for different domains (search, calculation, date, etc.).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lists operations and their required parameters (e.g., pattern for count_substring). It does not explicitly state when not to use this tool or give alternatives, but the context of sibling tools makes it clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It lists supported units and categories but omits details like error handling for mismatched categories or invalid units, which are important for an AI agent to anticipate.
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 plus an example, making it very concise. Key information is front-loaded with the purpose immediately stated.
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?
The tool is simple and the description covers categories and units adequately. No output schema exists, but the return format is implicitly clear for a conversion tool. Minor gap: no mention of precision or rounding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for each parameter. The description adds value by providing category groupings and a concrete example, enhancing understanding beyond the schema alone.
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 'Convert between units of the same category' and provides specific categories and examples. It distinguishes itself from sibling tools like calculator or random_gen by focusing on unit conversion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the tool's purpose and gives an example, but it doesn't explicitly state when to use it over alternatives or provide any preconditions. However, the context is clear enough for most use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It mentions 'safely' implying no side effects, but does not elaborate on error handling, return format, or limits. For a calculator, this is adequate but not exceptional.
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?
Two sentences are optimally concise: the first states purpose and safety, the second lists capabilities with an example. No filler—every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no annotations or output schema, the description is complete for a calculator tool: it covers all supported operations, constants, and provides a clear example. Additional details like return type are unnecessary given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with a basic explanation. The description adds significant value by detailing supported operators, functions, constants, and an example, going well beyond the schema's minimal 'mathematical expression' note.
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 uses a specific verb ('Evaluate') and resource ('mathematical expression'), lists supported operations, functions, and constants, and provides an example. It clearly distinguishes from sibling tools which are unrelated (search, text, datetime, etc.).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Although no explicit when-not or alternative tools are mentioned, the description makes it obvious this tool is for math evaluation. Given the sibling context, there is no ambiguity, so it scores well but lacks formal usage boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses the tool's behavior: it performs three read-only datetime operations. It specifies input formats (ISO 8601, natural language) and format string patterns, giving the agent clear expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured: it starts with a high-level summary, then breaks down each operation with examples. Each sentence adds value, though it could be slightly more concise.
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 tool with 5 parameters, three operations, and no output schema, the description covers input formats, required parameters per operation, and format string components. It is nearly complete, leaving little ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although the input schema already describes all parameters (100% coverage), the description adds meaning by explaining the context of each operation, how parameters combine, and the format string syntax. It goes beyond schema descriptions.
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 the tool's three operations: get current date/time, format a date, and calculate date difference. It distinguishes itself from sibling tools like calculator or random_gen by focusing specifically on datetime operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit instructions for when to use each operation (now, format, diff) and which parameters are required. However, it does not explicitly state when NOT to use this tool or list alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that the tool is a discovery step (read-only), describes the return format, and gives critical instructions on parameter handling. Minor missing info on permissions or side effects, but overall transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is relatively long but well-structured with sections and bullet points. Front-loaded with purpose and importance. Could be slightly trimmed but every section adds necessary context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 2 parameters, no output schema, and complexity (multiple domains, structured params), the description is very complete. It explains what is returned (markdown table), how to use results, and integrates with sibling tools like batch_search and search.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear enum descriptions. Description adds significant value: explains the mutual exclusivity of domain vs domains, strongly recommends using 'domains', shows example queries, and clarifies how to interpret the params column in results.
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 it is a domain discovery tool for vertical search, prerequisite for searches that use a domain. It distinguishes itself from sibling tools like anysearch_search by being step 1 and returning sub_domains and params.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-call guidance with a list of domains, recommends using the plural 'domains' parameter with examples, and explains how to use results (sub_domain as primary key, pass params via sub_domain_params, use batch_search for multiple sub_domains).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the full burden. It discloses that only HTML pages are supported (PDF/binary returns error), content is truncated at 50,000 characters, and URL must start with http:// or https://. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with markdown headers for when-to-use, when-not-to-use, and constraints. Front-loaded with purpose. Some slight redundancy (e.g., 'Best for' repeats earlier statements), but overall efficient and clear for the amount of guidance provided.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 required param, no output schema, no annotations) and diverse sibling tools, the description is highly complete. It covers usage context, constraints, error cases (PDF/binary), and return format (clean markdown). No major gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% description coverage on the url parameter. The description adds behavioral context beyond the schema, such as constraints (only HTML pages, truncation) and that the URL must start with http:// or https://, which is also in schema but reinforces it. Minor redundancy, but adds value.
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 it is a URL extraction tool for fetching web page content as clean markdown. It distinguishes itself from sibling tools like anysearch_search by specifying when to use it after search, and uses a specific verb-resource combination (extract URL content).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides extensive when-to-use and when-not-to-use guidance, including explicit contrasts with search snippets and specific scenarios such as when snippets are too short or user asks to read a URL. Clearly states alternatives like using search if snippet is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully covers behavioral traits: parallel execution, failure isolation, maximum 5 queries, dependency on get_sub_domains for vertical queries, and required parameter handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with headings and examples, but slightly verbose. Every sentence adds value, but could be more concise in places.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity and lack of output schema, the description is comprehensive: covers use cases, constraints, parameter rules, and provides multiple examples. No gaps identified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Adds significant meaning beyond the input schema by detailing each query item structure, required/optional fields, and the rule for passing non-applicable required params as empty strings, all illustrated with examples.
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 the tool's purpose: 'run multiple Anysearch queries in a single call'. It distinguishes from sibling tool 'anysearch_search' by emphasizing parallel execution and saving context space.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly recommends usage: 'Use batch_search instead of multiple sequential search calls when you have 2–5 independent queries'. Provides specific primary use case after get_sub_domains and alternatives for ambiguous queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden. It fully discloses the tool's behavior: it performs search with optional vertical routing, requires get_sub_domains for domain parameters, and outlines the workflow (search then optionally extract). There is no contradiction or hidden behavior; it clearly states the tool's read-only nature and dependencies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very long but well-structured with sections, bullet points, and examples. It is front-loaded with the most important info. However, some repetition (e.g., multiple similar examples) could be trimmed to improve conciseness without losing essential guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, nested objects, multiple siblings, no output schema), the description is remarkably complete. It covers all parameter usage, workflows (Path 1, Path 2, hybrid, multi-domain), critical rules, and interactions with siblings. It even addresses edge cases like borderline queries and ambiguous queries.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although schema coverage is 100%, the description adds substantial semantic value beyond the schema. It explains proper use of each parameter: query must have one intent, domain must come from get_sub_domains, sub_domain_params must be obtained and never invented, and max_results has a maximum of 10. It also provides guidance on handling required params with empty strings.
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 the tool's purpose as a search tool for external knowledge, with specific verb+resource ('search for current information, news, facts, etc.'). It distinguishes itself from siblings like batch_search and get_sub_domains by specifying when to use each, e.g., 'Use this for any query that requires looking up...' and explicitly differentiates Path 1 (general) vs Path 2 (vertical).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides extensive usage guidelines, including a decision tree, triggers for Path 1 vs Path 2, examples, and critical rules like 'NEVER call search with domain unless get_sub_domains was called first.' It explicitly states when not to use and offers alternatives like batch_search for multi-intent or hybrid queries.
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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