AI Impact MCP

by anchetadev

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have clearly distinct purposes (e.g., estimate_impact vs. log_usage), but some overlap exists: efficiency_score and analyze_efficiency both address efficiency, and record_web_chat and log_usage both handle logging. Descriptions mitigate confusion.
Naming Consistency4/5
Names consistently use lowercase with underscores (e.g., analyze_efficiency, set_scenario). Most follow a verb_noun pattern, but efficiency_score is noun_verb, and verbs vary (analyze, estimate, log, scan). Minor inconsistency.
Tool Count5/5
9 tools is well-scoped for tracking AI usage, estimating environmental impact, and reporting. Each tool has a clear role, with no obvious bloat or deficiency.
Completeness4/5
Covers core logging (manual, web, session scans), estimation, efficiency analysis, reporting, and scenarios. Missing CRUD for logged records (e.g., delete or edit) and export, but primary workflows are present.
Average 3.7/5 across 9 of 9 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 9 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 passing
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavioral traits. It mentions 'recorded AI usage' suggesting read-only, but does not state whether it modifies data, requires permissions, or has rate limits. Minimal transparency.
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?
Single sentence, efficient and front-loaded with the main action. No redundant information, but could benefit from clearer structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema; description does not specify return format or content beyond 'breakdown by model'. Given two parameters and no sibling differentiation, the description is insufficient for an agent to fully understand the tool's output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds no meaning beyond the schema for 'period' (already enumerated) and fails to explain the 'scenario' parameter (conservative, midpoint, high) which is critical for understanding impact calculations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool summarizes recorded AI usage and environmental impact broken down by model, which is a specific verb+resource. However, it does not differentiate from siblings like 'estimate_impact' which also deals with impact.
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?
No guidance on when to use this tool versus alternatives like 'estimate_impact' or 'scan_logs'. The description implies it's for summarizing recorded data, but lacks explicit context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It only states the action and effect (record into local store), but does not disclose potential side effects like overwriting or idempotency beyond what is obvious.
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, front-loaded with key information. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 7 parameters, no output schema, and low schema coverage, the description is too minimal. It does not guide on parameter usage, constraints, or return values, leaving significant gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 14%, and the description adds no parameter-specific meaning. For example, 'model' is required but unexplained; defaults like input_tokens=0 are unaddressed. The description fails to compensate for low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'record' and resource 'token usage', with the purpose of making it show up in reports. It distinguishes from siblings by mentioning manual logging.
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 this to manually log usage from any client', indicating when to use the tool. Does not provide exclusions or alternatives, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility. It discloses that the tool generates an HTML dashboard and returns a file path, but does not mention any side effects, authentication requirements, or whether it modifies state. Minimal behavioral context.
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 filler, front-loaded with the core action and result. Every word adds value.
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 3 parameters, no annotations, and no output schema, the description covers the main function and output but misses preconditions (e.g., having recorded usage) and parameter details. It is minimally adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 33% (only 'path' has description). The description does not explain the parameters beyond the implied output path. It does not clarify 'scenario' or 'days' meaning, leaving gaps for an agent to infer.
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: building a standalone HTML dashboard from recorded usage data, and returns a file path. The verb 'Build' and resource 'dashboard' are specific, and it distinguishes itself from siblings like 'analyze_efficiency' or 'estimate_impact'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a visual dashboard is needed, but does not explicitly guide when to use this tool versus alternatives (e.g., for efficiency analysis vs. impact estimation). No when-not-to-use or alternative tools 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 full burden. It discloses the output format (score, grade, actionable tips) but does not mention potential side effects, mutability, permissions, or error behavior. The transparency is adequate but not thorough.
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 short sentences with no redundant information. Every word contributes to explaining the tool's function, input, and output. It is appropriately sized and 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 tool's simplicity (one required parameter, no output schema), the description covers the core behavior adequately: it returns a score, grade, and tips. However, it lacks context on how the grade is interpreted or any scenario guidance relative to siblings, which would enhance completeness.
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 the schema already defines the 'turns' parameter with properties. The description merely restates that the tool takes conversation turns, adding no new semantic detail beyond the schema. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'scores' the efficiency of conversation setup, specifying the input (conversation turns) and output (score, grade, tips). It is specific enough to differentiate from siblings like 'analyze_efficiency', though that sibling might have a similar purpose without further details.
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 includes the instruction 'Pass the conversation turns', which provides basic usage guidance. However, it does not elaborate on when to use this tool versus alternatives (e.g., when to use 'analyze_efficiency' instead) or mention any prerequisites or limitations.
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 full burden for behavioral traits. It discloses the methodology (EcoLogits) but does not explicitly state read-only nature, side effects, or limitations. The word 'estimate' implies computation without mutation, which is helpful but not fully 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 sentences long, front-loading the output and input in the first sentence and methodology in the second. Every sentence is substantive with no waste.
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, the description mentions the key output metrics (kWh, miles, water, CO2e), which is important context. However, it does not specify the return format, structure, or error handling. For a tool with 5 parameters, it is fairly complete but leaves minor gaps.
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 80%, so baseline is 3. The description adds minimal semantic value beyond the schema, only mentioning 'token counts' generally. It does not elaborate on model, scenario, or zone parameters, which are already described in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool estimates environmental impact (energy kWh, miles, water, CO2e) for a single AI request given token counts. It specifies the methodology (EcoLogits). However, it does not explicitly differentiate from siblings like analyze_efficiency or efficiency_score, which may have overlapping 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 implies usage for estimating impact of a single request based on token counts, but does not provide explicit guidance on when to use vs alternatives. There are no when-not or exclusion criteria mentioned, leaving the agent to infer context.
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 data is read on-demand and never stored, which is important for privacy. No annotations provided, so description carries full burden. Lacks details on authentication or rate limits but covers key 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficient sentences: first states action and key behavioral note, second lists return values. No unnecessary words, front-loaded with important information.
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?
Describes purpose, behavior, and return values adequately for a simple tool, but misses documenting the 'dir' parameter and does not clarify how sessions are selected or what 'recent' means relative to limit. With no output schema, return values are listed but not detailed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50% (limit has description, dir lacks it). Description does not explain the 'dir' parameter, leaving ambiguity. It adds context for limit through 'recent sessions' but insufficient for overall parameter clarity.
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 runs an efficiency coach over recent Claude Code sessions, reads transcript text on-demand without storing, and returns specific metrics. Distinguishes from siblings by focusing on session 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?
Implies usage for analyzing recent sessions with a limit parameter, but does not explicitly state when not to use or compare with siblings like efficiency_score or estimate_impact.
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 description explains the effect on future estimates and defines each option, but does not disclose scope (e.g., session persistence), side effects, or whether it overwrites previous settings. No annotations are present.
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 fluff, critical details front-loaded. Every word earns its place.
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 simple setter with one parameter and no output schema, the description is largely complete. It explains the effect and options, though it could mention the return value or confirmation.
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 description adds meaning to the enum parameter by explicitly defining each value ('conservative = lowest...', etc.), compensating for zero schema description coverage. The definitions are clear and concise.
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 sets the default confidence scenario for future estimates, with a clear verb ('Set') and resource ('default scenario'). It distinguishes itself from sibling tools, which focus on analysis, scoring, or logging.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage before making estimates, but lacks explicit guidance on when to use or avoid this tool. No alternatives are mentioned, though no sibling directly conflicts.
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 does well by disclosing token estimation via BPE proxy, the tagging as 'estimated', the two input modes, and the model default. It does not mention failure behavior or side effects, but for a logging tool this is adequate.
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 sentences, front-loaded with purpose, no wasted words. Every sentence adds value (purpose, preferred input, fallback, estimation detail).
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 (estimation, two input modes, optional params) and no output schema, the description covers the key aspects. Minor gap: behavior when both turns and page_text are provided is unspecified, and conversation_id is not explained. Otherwise 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 75% (3 of 4 params described), and the description adds significant meaning by explaining the two input methods (preferred vs fallback) and estimation behavior. The conversation_id param lacks description in both schema and description, but overall value is above baseline.
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 it records estimated usage for Claude web chats, distinguishes between structured turns and fallback page_text input, and mentions token estimation. This is specific and distinct from sibling tools that analyze or score data.
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 when to use (for conversations without token counts) and gives a preferred method with a fallback. However, no explicit when-not-to-use or alternatives are named.
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 provided, the description fully discloses behavior: it reads token counts and metadata, never message content, is idempotent, and safe to run repeatedly. It could briefly mention permission requirements (e.g., read access to ~/.claude/projects), but overall good transparency.
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 sentences with no wasted words: first sentence states purpose, second adds scope and constraints, third confirms idempotency. Information is front-loaded.
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 a simple tool with 1 optional parameter and no output schema, the description covers purpose, input, behavioral constraints, and safety. Sibling tools are sufficiently distinct, so no further context is needed.
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% for a single optional parameter 'dir'. The description adds the default directory path (~/.claude/projects) and the meaning 'override the Claude Code projects directory', providing more context than 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?
Description clearly states the verb 'Backfill', the resource 'exact AI usage from Claude Code's local session transcripts', and scope 'reads only token counts + metadata, never message content'. It differentiates from siblings like analyze_efficiency by focusing on raw log scanning rather than 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?
Description explains when to use (backfill exact AI usage) and emphasizes it is idempotent and safe to run repeatedly. It specifies what it reads (token counts + metadata) and crucially what it does not read (message content), but does not explicitly mention when not to use or alternatives among sibling tools.
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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