tokencast

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Server Quality Checklist

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Latest release: v0.1.4
Disambiguation5/5
Each tool targets a distinct concern: telemetry opt-out, cost estimation, calibration metrics, historical queries, and cost reporting at different granularities. No two tools overlap in purpose or action.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in lowercase snake_case (disable_telemetry, estimate_cost, get_calibration_status, get_cost_history, report_session, report_step_cost). The naming style is uniform and predictable.
Tool Count5/5
Six tools is well-scoped for a cost estimation and calibration server. Each tool contributes a necessary function without redundant or bloated additions.
Completeness4/5
The toolset covers the essential workflow: estimate costs, report actuals, inspect calibration, and query history. Minor gaps exist (e.g., no way to enable telemetry after disabling, no per-estimate detail retrieval), but they do not break the core usage flow.
Average 3.3/5 across 6 of 6 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on March 31, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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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?
With no annotations, the description carries the full burden. It states the action but does not disclose any behavioral traits like whether this is a read-only operation, data transmission, or any side effects. No detail is provided about how the report is stored or used.
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 with no filler words, focusing on the core action and purpose. It is appropriately brief, though it under-specifies details that are penalized in other dimensions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has four parameters, one required, and a nested object, with no output schema or annotations. The description gives no information about required inputs, format, or expected behavior, making it inadequate for correct usage.
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 mention any parameters. The tool has four parameters including a required actual_cost and a nested step_actuals object, but the description fails to explain their meaning or relationships.
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 uses a specific verb ('Report') and resource ('actual session cost'), and notes the purpose of improving calibration. It doesn't explicitly distinguish from sibling tools like report_step_cost, but the session-level scope provides some differentiation.
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 such as report_step_cost or estimate_cost. The phrase 'to improve future calibration' hints at a post-session reporting context, but no exclusions or alternatives are mentioned.
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 the full burden of behavioral disclosure. It only states 'Query' (implying a read operation) but does not disclose return format, data scope, cost implications, or any side effects. This is insufficient for a tool with no annotation support.
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 with no filler, achieving good conciseness. However, it is so brief that it sacrifices informative content, though it does front-load the core purpose effectively.
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?
Given the tool's simplicity and lack of annotations or output schema, the description is incomplete. It does not explain what the history records include, how the 'window' parameter behaves, or what 'include_outliers' does, leaving an agent without enough context to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has two parameters ('window' and 'include_outliers') with no descriptions, and schema description coverage is 0%. The description does not explain or even mention these parameters, leaving their meaning and valid values completely undocumented.
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 'Query' and identifies the resource as 'historical cost estimation records and actuals.' It clearly distinguishes itself from sibling tools like 'estimate_cost' (which estimates) and 'report_step_cost' (which reports individual costs), positioning this as the tool for historical queries.
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 is provided on when to use this tool versus alternatives. The description does not mention typical use cases, prerequisites, or exclusions, leaving the agent to infer its role from the name alone.
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 for behavioral disclosure. It states the tool estimates costs, implying a non-mutating calculation, but does not reveal whether it makes network calls, how the estimate is computed, what the return format is, or any side effects. For a tool with 10 parameters, this is insufficient.
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, front-loaded sentence of under 15 words. It contains no fluff and efficiently communicates the core purpose, achieving high clarity per word.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is complex (10 params, no output schema, no annotations), yet the description provides only a minimal one-sentence purpose. It gives no information about expected inputs, output structure, or behavioral nuances, making it wholly inadequate for an agent to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, and the tool description does not explain any of the 10 parameters. Required fields like size, files, and complexity are not mentioned, and even the enums (XS/S/M/L, low/medium/high) are left undocumented. The description fails to compensate for the schema's lack of semantics.
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 ('Estimate') and identifies the resource ('Anthropic API token costs') and context ('for a development plan before execution'). This clearly distinguishes it from sibling tools that report or record costs, making its purpose unambiguous.
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 phrase 'before execution' provides clear contextual guidance for when to use this tool, implying it is for planning rather than after-the-fact reporting. However, it does not explicitly name alternative tools or state when not to use it, so it falls short of the highest bar.
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 of behavioral disclosure. As a 'get' operation, it implies read-only behavior, and 'health and accuracy metrics' adds some context. But it does not mention data format, side effects, or prerequisites beyond the name.
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, focused sentence that front-loads the action and object. Every word contributes meaning, and there is no fluff or redundancy.
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 no output schema or annotations, the description leaves key details undefined: the meaning of the 'window' parameter, the exact nature of the returned metrics, and any behavioral caveats. While the tool is simple, the missing parameter documentation makes it incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter ('window') with 0% description coverage, and the description does not mention it at all. The agent has no way to know what 'window' accepts or means, making this a critical gap that the description fails to compensate for.
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 ('Get') and resource ('calibration health and accuracy metrics for cost estimates'), clearly distinguishing this from sibling tools like estimate_cost or get_cost_history. It immediately conveys the tool's purpose without ambiguity.
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 usage is implied: one would call this to review calibration status. However, there is no explicit guidance on when to use it versus alternatives, nor any exclusion criteria. It lacks clear context about the intended scenario.
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?
Since no annotations are provided, the description carries the burden of behavioral disclosure. It does disclose the accumulation and flushing behavior, which is key context. However, it does not mention whether this is a mutation operation, any side effects, authentication requirements, or what happens on success/failure. The provided behavior is useful but incomplete for a write tool with no annotation coverage.
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 exactly two sentences. The first sentence states the purpose directly, and the second provides critical behavioral context about accumulation and flushing. Every word is necessary, with no filler or redundancy.
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?
The description covers the essential behavioral context (accumulation and flushing) that is not evident from the schema. However, for a tool with 7 parameters, no annotations, and no output schema, it lacks details about parameter usage, required fields, and what happens during execution. It is not completely inadequate but leaves significant gaps in how to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 7 parameters with 0% description coverage. The description does not mention any parameter names, meanings, or constraints beyond the generic phrase 'cost of a completed pipeline step.' It fails to explain required parameters (step_name) or the token fields, leaving the agent to infer from names alone. The description adds no value to parameter semantics.
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 specific action: 'Record the cost of a completed pipeline step.' This uses a precise verb ('Record') and identifies the resource ('cost of a completed pipeline step'). It also distinguishes itself from siblings by focusing on per-step cost recording, whereas siblings like estimate_cost and report_session 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains that costs accumulate per step and are flushed when report_session is called, implying this tool is for incremental recording before a final session report. This provides clear context for when to use it, though it does not explicitly name alternatives or state when not to use it. The sibling tools (estimate_cost, report_session) are implicitly differentiated by this behavior.
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 carries full burden. It discloses the file creation side effect (~/.tokencast/no-telemetry), the permanence of the action, and clarifies the scope to anonymous telemetry. It doesn't mention reversibility or impact on already collected data, but for a simple opt-out toggle, 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?
Two crisp sentences, front-loaded with the primary verb and purpose, with no filler. Every word adds value, including the file path and opt-out 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?
For a zero-parameter, no-output-schema tool, the description fully explains what it does, how it does it (file creation), and when to use it. Further elaboration would be redundant 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?
The tool has zero parameters, and the schema is 100% covered (though empty). The description doesn't need to explain param semantics since none exist, and it still clarifies the tool's behavior without them.
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 action ('Permanently disable anonymous telemetry'), identifies the specific resource, and even mentions the side effect of creating a file. This clearly distinguishes it from sibling tools focused on cost estimation and reporting.
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 phrase 'Use this to opt out of usage data collection' provides explicit when-to-use guidance. No alternatives are needed since sibling tools serve entirely different purposes (cost, calibration, reporting), making the intended use unambiguous.
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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