infra-advisor-mcp

by yangsong7

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose, from task analysis to cost estimation and report generation. No overlapping functionalities; descriptions clearly differentiate between inference cost, maintenance, training, and comparison tools.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (e.g., analyze_task, estimate_inference_cost, generate_full_report). No mixing of conventions or vague verbs.
Tool Count5/5
With 12 tools, the server covers the full lifecycle of infrastructure planning without being bloated. Each tool serves a necessary function, and the count is ideal for the domain.
Completeness5/5
The tool set comprehensively addresses the domain: task analysis, model recommendation, inference/training/maintenance cost estimation, TCO comparison, data freshness checks, and report generation/saving. No obvious gaps for the stated purpose.
Average 4.1/5 across 12 of 12 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 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 MIT License.
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.
If you are the author, simply .
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.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the return includes ranked models with pricing, strengths, and caveats, implying a read-only operation. However, it does not explicitly mention safety, rate limits, or any potential side effects, leaving some ambiguity.
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 extremely concise with three sentences, each delivering distinct value: purpose, usage tip, and output summary. No extraneous words, and the most critical information appears first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (7 parameters, no annotations) and the presence of an output schema, the description lacks parameter explanations. It does provide a usage link to 'analyze_task', partially compensating. Still, the agent would need to infer parameter semantics from names, which is a significant gap.
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?
Schema description coverage is 0%, and the description does not explain the 7 parameters (use_case, domain, scale, etc.) beyond passing them from 'analyze_task'. The agent must infer meanings from parameter names alone, which is insufficient for correct invocation.
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 recommends ranked open-source and closed-source models for a task, specifying the verb 'recommend', resource 'models', and scope 'open-source and closed-source'. This distinguishes it from sibling tools like 'analyze_task' (analyzes task) and 'compare_cloud_vs_onprem' (compares hosting).
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 advises passing parameters from 'analyze_task' output for best results, providing a context for use. However, it does not explicitly state when not to use this tool or mention alternatives like 'compare_cloud_vs_onprem' or 'estimate_inference_cost' that might be more appropriate for specific 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?
With no annotations provided, the description carries the full burden. It discloses the tool's purpose and return values, but does not mention side effects, authentication needs, rate limits, or data sources. The behavior is clear but not deeply transparent.
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 concise sentences with no wasted words. It front-loads the core purpose and immediately specifies the return values, making it efficient for agent parsing.
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 presence of an output schema and fully described parameters, the description is complete enough. It notes the key outputs (cumulative costs, break-even, recommendation). However, it could mention assumptions or data freshness for a slight improvement.
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?
Input schema coverage is 100%, so the schema already provides full parameter descriptions. The tool description adds no extra meaning beyond what the schema offers, thus meeting baseline expectations.
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 compares total cost of ownership between cloud and on-prem, over specific horizons (1/3/5 years), and returns cumulative costs, break-even month, and a recommendation. This verb+resource combination is specific and distinguishes it from sibling tools like estimate_inference_cost or estimate_training_cost.
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 the tool is used when needing a TCO comparison, but it does not explicitly state when to use or avoid it, nor does it mention alternatives. It lacks when-not-to-use guidance or prerequisites, leaving the agent to infer appropriate context.
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 provided, so description bears full burden. It mentions using Chinchilla scaling laws and explains LoRA/QLoRA behavior. But it omits limitations, assumptions, or accuracy notes, leaving gaps in 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 a dense paragraph covering many points. It is front-loaded with the core purpose but could be more structured (e.g., bullet points) for better readability. Some sentences could be merged.
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 presence of an output schema (not shown but known from context), the description does not need to detail return values. It covers training types, algorithm, and parallelism recommendation. Missing explicit prerequisites or context requirements, but overall sufficient for an estimation tool.
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% and describes all parameters well. The description adds minor value (e.g., 'sensible defaults' for dataset_tokens, enumeration of GPU types) but doesn't significantly extend beyond schema.
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 estimates GPU-hours, wall-clock time, cost, and sharding strategy for training runs, with specific verb 'estimate' and resource 'training cost'. It lists covered training types and distinguishes from sibling tools like estimate_inference_cost and estimate_maintenance_cost.
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 when to use the tool (for various training runs) and differentiates between training types (e.g., LoRA/QLoRA vs full fine-tuning). However, it does not explicitly state when not to use it or direct to alternatives, though sibling tools provide implicit differentiation.
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 carry the full burden. It lists included cost categories but does not disclose any behavioral traits (e.g., whether the estimation is read-only, if it requires permissions, or if it triggers any side effects). The description is adequate but not detailed.
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 concise with two short sentences. It front-loads the primary purpose and then lists what is included. No wasted words.
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 output schema exists, the description does not need to explain return values. It covers the tool's purpose, scope, and inputs adequately. All parameters are described in the schema.
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% (all 4 parameters described). The description adds context about cost categories but does not enhance understanding of individual parameters beyond the schema. Baseline 3 applies.
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 estimates 'all ongoing on-prem operational costs for a GPU cluster' and enumerates cost categories (power, cooling, etc.). This distinguishes it from sibling tools like 'estimate_inference_cost' and 'estimate_training_cost', which focus on compute costs for specific tasks.
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?
No explicit guidance is given on when to use this tool versus alternatives. The context implies it is for on-prem maintenance cost estimation, but there is no 'when-not-to-use' or comparison to siblings like 'compare_cloud_vs_onprem'.
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 provided, the description carries the full burden. It indicates a read operation ('list') but does not explicitly state that it is safe, idempotent, or free of side effects. For a listing tool, this is adequate but not rich—it lacks details on authentication or data freshness.
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 ('List all GPU types') and adds context ('with specs and pricing'). It is concise and contains no unnecessary words.
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 that the tool has no parameters and an output schema exists, the description is mostly complete. It clearly states what is returned. However, it could mention that the list is unfiltered (all GPUs) and possibly include note about sorting or pagination, but overall it is sufficient.
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?
There are zero parameters, so the baseline is 4. The description adds no parameter-specific meaning, but none is needed. It correctly implies the tool returns all GPUs without filters.
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 lists all GPU types in the database with specs and pricing, which is a specific verb-resource pair. It distinguishes itself from sibling tools that focus on analysis, estimation, or reporting (e.g., estimate_training_cost, compare_cloud_vs_onprem).
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 the tool is used when the user wants to see available GPU options, but it does not provide explicit guidance on when to use it versus alternatives like compare_cloud_vs_onprem or recommend_model. No exclusions or prerequisites are mentioned.
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 full burden. It mentions saving to .md and .html files and that output_dir is 'created if needed', but does not disclose what happens if files already exist (overwrite? append?), any authentication requirements, or possible side effects.
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 concise: two sentences with the main action front-loaded. Every sentence adds value with no 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?
The tool has an output schema (though not shown), so the description need not explain return values. It covers the core functionality adequately, but could mention what the tool returns (e.g., success confirmation or file paths).
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 description coverage is 100%, so the schema already documents all parameters well. The description adds minor context by mentioning 'Pass all follow-up answers', but does not significantly extend beyond the schema descriptions. Baseline 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 the action ('Save') and the resources (final report and follow-ups to .md and .html files). It also implicitly distinguishes itself from sibling tools like generate_full_report and generate_followup_answer, which focus on generation rather than saving.
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 tells when to call the tool ('when the user is satisfied with the report — this is the explicit finalize action') and what to pass ('Pass all follow-up answers accumulated during the session'). It does not explicitly state when not to use it, but the context is clear enough.
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 takes full burden. It details self-hosted sizing logic involving quantization and latency, and what returns are provided (break-even, topology, replicas). 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?
Description is a single paragraph with front-loaded purpose. It is informative but could be more structured (e.g., bullet points). No wasted sentences.
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 tool complexity and existence of an output schema (not shown), description adequately explains return values and key levers. It covers self-hosted sizing and break-even analysis sufficiently.
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 71%; description adds meaningful context for quantization and latency (e.g., 'shrinks model VRAM' and 'latency target sizes replicas'), but does not elaborate on required parameters beyond schema.
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?
Description clearly states it compares cloud API and self-hosted inference costs for a given token volume, and distinguishes from siblings like estimate_training_cost or estimate_maintenance_cost by focusing on inference cost estimation specifically.
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?
Usage is implied but not explicitly guided: no statements on when to use vs alternatives like compare_cloud_vs_onprem. The description focuses on what it does, not when to invoke it.
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; description discloses sequential execution and report coverage, but omits potential side effects or execution time.
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, no wasted words; first sentence states action, second elaborates on content.
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?
Complete enough given output schema exists; covers purpose, scope, and composition; could mention it is a heavy operation.
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% and parameter description in schema already clarifies 'Plain English description'; tool description adds no extra semantic 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?
Description states 'Generate a comprehensive markdown infrastructure report' and clarifies it runs all tools in sequence, distinguishing it from sibling tools that are individual components.
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?
Explicitly labeled 'main entry point', implying use for full reports, but does not explicitly state when to avoid it, e.g., for partial analysis.
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 provided; description only states it returns timestamps. Does not disclose if it requires authentication, rate limits, or side effects. For a read-only operation, the behavior is implied but not explicit.
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 short sentences: first states what it does, second gives usage guidance. No unnecessary words, front-loaded with purpose.
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 and the presence of output schema, the description covers purpose and use case. Could add a note about being read-only or safe to call frequently, but overall adequate.
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?
No parameters in input schema, so description adds value by explaining the output (timestamps) and use case. Schema coverage is 100%, baseline is 3, but the description provides meaningful context beyond the empty schema.
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 verb 'Return' and the resource 'last_updated timestamps for all data entries', distinguishing this tool from siblings like estimate costs or analysis.
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?
Explicitly says 'Use this to check if pricing data is stale before relying on estimates', providing a specific use case. Lacks explicit when-not-to-use or alternatives, but the context is 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 carries the full burden. It discloses the key behavior—reloading data from disk without restarting—and mentions it picks up updated pricing. However, it does not detail side effects (e.g., whether in-memory state is cleared, if it is safe for concurrent access, or any prerequisites). This is adequate for a simple reload tool but lacks depth.
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 long, front-loaded with the core action, and every word adds value. No wasted text.
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 with no parameters and an output schema that likely documents return values. The description covers purpose and usage context adequately. It could mention if it requires special permissions or if it is a non-destructive operation, but overall it is sufficiently complete for its complexity.
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?
The tool has zero parameters, and the input schema is an empty object with 100% coverage. Per the rule for 0 parameters, baseline is 4. The description does not need to add parameter details, and it does not mislead.
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?
Description clearly states it reloads all YAML data files from disk without restarting the server. The verb 'reload' and resource 'YAML data files' are specific, and the description implicitly distinguishes this administrative tool from the analytical sibling tools (e.g., analyze_task, compare_cloud_vs_onprem).
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?
Explicit guidance is given: 'Call this after running sync scripts to pick up updated pricing.' This provides a clear use case and context. It does not explicitly state when not to use, but the guidance is sufficient for the tool's purpose.
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?
Discloses that returns structured data including scale, use_case, domain, latency, and token volumes. While no annotations are present, the description is transparent about the tool's output and purpose, though it does not mention safety or side effects (likely benign).
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?
Three focused sentences: first on action, second on usage positioning, third on output. No redundant words; perfectly front-loaded.
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 presence of an output schema, the description's list of returned fields is sufficient. It provides a complete picture for the agent to understand input and output expectations.
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?
The input parameter 'task_description' has no schema description, but the tool description explains it as 'free-text task description,' adding meaning beyond the schema. It compensates for the 0% coverage with clear context.
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 parses free-text into structured parameters, with specific verb 'Parse' and resource 'free-text task description'. It distinguishes from sibling tools like 'compare_cloud_vs_onprem' by positioning itself as an initial analysis step.
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 advises to 'Use this first to understand what the user needs before calling other tools,' giving clear guidance on when to invoke it within a workflow.
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 answer is 'calculator-backed' (implies computation) and returns specific components. However, it does not explicitly mention idempotency, permission requirements, or side effects. Still, the description gives a good overview of behavior without hiding critical traits.
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?
Four sentences, each serving a purpose: main function, usage guideline, examples, return components. No redundant text, well-structured and efficient.
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 moderate complexity (two string parameters, output schema exists), the description covers when to use, what to expect, and differentiates from sibling tools. It is complete enough for an agent to correctly select and invoke the tool.
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 both parameters. The tool description adds meaning by clarifying that the followup_question should be focused and provides examples. This goes beyond the schema by giving usage context, though the schema already adequately describes parameters.
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?
Description clearly defines that the tool answers specific follow-up questions with calculator-backed data and an inline glossary. It explicitly distinguishes itself from generate_full_report by stating when to use this tool instead, and lists the components of the return (direct response, data table, recommendation, jargon glossary).
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 states when to use: 'when the user asks a focused follow-up' and provides concrete examples like 'what's the training cost?'. Also says to use generate_full_report instead for full reports, giving clear guidance on 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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