CIA Diagnose

Server Quality Checklist

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

Latest release: v1.3.0
Disambiguation5/5
Each tool has a distinct purpose with no overlap: quick_scan offers a free quick diagnosis, business_diagnose provides a full diagnosis, action_plan generates a roadmap, roi_projector projects ROI, tools_recommend suggests tools, case_studies shows success stories, contact_cia gives contact info, export_report creates a report, and list_industries lists available industries. Agents can clearly differentiate them.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in lowercase with underscores (e.g., business_diagnose, action_plan, roi_projector). No mixing of conventions or styles, making it predictable and easy to interpret.
Tool Count5/5
With 9 tools, the set is well-scoped for the business diagnosis domain. It covers the entire customer journey from initial quick scan to detailed diagnosis, planning, ROI projection, recommendations, social proof, contact, and report export. No tool feels extraneous or missing.
Completeness5/5
The tool surface is complete for the stated purpose: it includes lead capture, full diagnosis, action planning, financial projection, tool recommendations, case studies, contact information, report export, and industry benchmarks. There are no obvious gaps that would hinder an agent from fulfilling the diagnosis-to-action workflow.
Average 4.3/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 15 commits in the last 12 weeks
- Last stable release on July 14, 2026
- 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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint=true, destructiveHint=false, idempotentHint=true) already indicate a safe read operation. The description adds value by detailing the return structure (company info, pricing, booking link, contact details), though it does not disclose any additional behavioral traits.
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 contains a lengthy sales pitch ('This is where value converts to action...') that does not add operational value for an AI agent. The core functional info is front-loaded, but the fluff reduces conciseness. Adequate but not optimal.
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 has a single optional parameter, rich annotations, and an output schema, the description provides sufficient context. It covers the return fields comprehensively for a contact retrieval tool, though it omits details about error handling or invalid input behavior.
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 schema has 0% description coverage, so the description must compensate. It adds meaning by specifying the 'lang' parameter as Language with allowed values 'es' or 'en', which is not present in the schema. This adequately clarifies the only parameter.
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 retrieves contact information, services catalog, and booking options. This verb+resource combination is specific and distinct from sibling tools like action_plan or quick_scan, which serve different purposes.
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 suggests using the tool when 'the client is ready to take the next step,' providing some context, but lacks explicit when-not-to-use guidance or comparison to siblings like tools_recommend or roi_projector, which could also be relevant at conversion stages.
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?
The annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering safety and idempotency. The description adds that it is free and requires no context, but does not significantly extend behavioral understanding beyond the annotations. 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?
The description is relatively concise, with a clear header line followed by usage guidance and parameter descriptions. The Args and Returns sections are structured. However, some formatting (e.g., dashes in 'Returns:' block) could be cleaner, but overall it is front-loaded and efficient.
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 has no required parameters and a simple output, the description covers the key inputs, output structure (including next_step and is_free), and usage context. It does not address error behavior or edge cases, but for a straightforward read-only scan, 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?
The schema has 0% description coverage, but the tool description includes an Args section that describes each parameter (company_name, industry, description, lang) with brief semantics, such as the purpose of lang for language selection. This compensates well for the lack of 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 performs a 'FREE quick scan' that identifies '3 generic problems' from a company name and optional description. It specifies the output and distinguishes itself from the sibling 'business_diagnose' by positioning itself as a precursor.
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 explicitly advises using this tool 'before proposing business_diagnose', providing clear guidance on when to use it relative to a sibling. It implies it is for a quick, free assessment but does not explicitly state when not to use it or mention alternatives beyond the one sibling.
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?
Annotations already declare readOnlyHint and idempotentHint. Description adds valuable behavioral context: returns case studies with before/after metrics, timeline, tools, and ROI. No contradictions with annotations.
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?
Front-loaded with main purpose, structured with Args/Returns. One persuasive sentence ('Social proof is the most powerful sales tool.') is mildly extraneous but does not detract significantly.
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?
Covers purpose, return fields, and usage context with roi_projector. For a simple read-only list tool with optional parameters and output schema, it is sufficiently complete.
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 0%, so description compensates by explaining each parameter: industry filter, dimension filter, language (es/en). Adds meaning beyond schema titles and defaults.
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 starts with a clear verb and resource: 'Show before/after transformation stories from real implementations.' It distinguishes case_studies from siblings like roi_projector (projection) and business_diagnose (diagnosis) by focusing on real stories with metrics.
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 advises using roi_projector before this tool to validate numbers, providing a usage sequence. However, it lacks explicit when-not-to-use guidance or comparison to other siblings.
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?
Annotations already indicate read-only and idempotent behavior. The description adds that the plan is prioritized by ROI and ease, and that each action includes DIY and CIA-assisted options. No contradictions with annotations.
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 a clear purpose statement, usage note, and parameter list. It is comprehensive but not overly verbose; however, it could be slightly more concise without losing essential details.
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 complexity (6 parameters, no schema descriptions), the description thoroughly explains parameters and returns. It mentions the prerequisite of business_diagnose. Minor gaps: no discussion of error handling or rate limits, but annotations cover safety.
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 description coverage is 0%, so the description must carry the full burden. The description includes a detailed Args section explaining each parameter, including types, defaults, and examples. This adds significant meaning beyond the bare 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 it generates a prioritized 30/60/90 day action plan based on diagnosis, with specifics like prioritizing by ROI and ease, and including DIY and CIA-assisted options. It distinguishes itself from sibling tools by explicitly stating it should be used after business_diagnose.
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 guidance to use AFTER business_diagnose, establishing a clear use case and prerequisite. It does not discuss when not to use or alternative tools, but the context is sufficient for an AI agent to understand the intended 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?
Annotations already provide readOnlyHint, destructiveHint, idempotentHint. Description adds value by noting weekly refresh, per-industry specialization, and that each tool includes 'why_best'. No contradiction.
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 summary, context, usage guidance, and args. Approximately 150 words, clear and front-loaded. Minor redundancy but overall 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?
No output schema, but description explains return format (dict grouped by dimension with name, tier, url, description, why_best). Also covers weekly refresh and tool registry. Complete for a read-only recommendation 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 description coverage is 0%, but the description compensates with detailed Args: lists options for dimensions, explains industry as ICP id, and lang as 'es' or 'en'. Adds meaning 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 'Recommend the BEST free/OSS/paid tools per business dimension,' specifying verb and resource. It distinguishes from siblings by referencing the curated tool registry and usage after business_diagnose.
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 states 'Use AFTER business_diagnose' and explains when to pass industry. Does not explicitly mention when not to use, but provides clear context for appropriate use.
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?
Annotations already indicate readOnly, idempotent, and non-destructive. Description adds behavioral context by explaining the return structure (industries list, Spanish and English notes) and the concept of calibrated vs generic benchmarks, which is helpful beyond annotations.
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?
Description is concise with front-loaded purpose followed by return details in a clear, structured manner. Every sentence adds value.
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 (no params, output schema exists), the description fully explains what is returned and the differentiation between Tier 1 and generic benchmarks. No 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?
No parameters exist, so baseline is 4. The description does not need to add parameter information.
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?
Clearly states the tool lists available industry benchmarks for CIA diagnosis, distinguishing between calibrated (Tier 1) and generic. The verb 'list' and resource 'industries with benchmarks' are specific, and the purpose is distinct from siblings.
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?
Provides clear context on what the tool returns (calibrated industries and generic benchmark availability), which implicitly guides when to use it. No explicit alternative or exclusion, but context is sufficient.
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?
Adds value beyond annotations by detailing output structure (current leak estimate, recovery levels, payback period, 12-month projection). Annotations already cover safety traits; description complements with behavioral specifics.
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?
Well-structured: intro, usage note, Args, Returns. Every sentence is informative and no verbosity. 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?
Covers input parameters, usage order, and output summary. Output schema exists externally, so detailed return structure not required. Slightly lacking in output schema detail but sufficient.
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?
Given 0% schema coverage, the description fully explains each parameter: monthly_revenue ('any currency'), revenue_leak_score ('Business Health Score from diagnosis 0-100'), team_size, currency, lang. Adds essential semantics missing from 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 projects ROI from diagnosis recommendations, with specific verb 'Project ROI' and resource 'diagnosis recommendations'. It distinguishes from siblings by indicating usage order.
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 AFTER business_diagnose or action_plan', providing clear context. Lacks explicit 'when not to use', but the instruction is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![cia-diagnose MCP server](https://glama.ai/mcp/servers/davidscoreal/cia-diagnose/badges/card.svg)](https://glama.ai/mcp/servers/davidscoreal/cia-diagnose)

Score Badge

Copy to your README.md:

[![cia-diagnose MCP server](https://glama.ai/mcp/servers/davidscoreal/cia-diagnose/badges/score.svg)](https://glama.ai/mcp/servers/davidscoreal/cia-diagnose)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/davidscoreal/cia-diagnose'

If you have feedback or need assistance with the MCP directory API, please join our Discord server