Polymath MegaBlaster MCP

Server Quality Checklist

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Latest release: v2.0.0
Disambiguation4/5
Each tool performs a clearly distinct utility operation; there is minimal overlap. The closest pairs are parse_date/convert_timestamp and diff_text/levenshtein_distance, but their descriptions differentiate them well.
Naming Consistency2/5
Naming style is mixed and inconsistent. Some tools use verb_noun (format_json, hash_text), some use noun_verb (base64_encode), some are bare nouns (factorial, permutations), and others use unconventional order like date_add rather than add_date.
Tool Count2/5
27 tools is large and more than needed for a coherent toolkit. The broad misc-utility scope inflates the count beyond the 3-15 well-scoped range, and many functions could be grouped into a smaller number of multi-purpose tools.
Completeness3/5
Covers many common utility categories like encoding, date math, math, and conversion, but the set is an arbitrary grab-bag. Obvious developer-utility gaps remain (URL/HTML encoding, string case manipulation, random generation, regex helpers), so coverage is useful but not comprehensive for a general-purpose toolkit.
Average 3.7/5 across 27 of 27 tools scored. Lowest: 2.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 21 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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.
This repository includes a glama.json configuration file.
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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the conversion action and says nothing about whether the operation is pure, what input formats are accepted, how output is formatted, or whether any transformations like prefix stripping or case handling occur.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words. It front-loads the operation and immediately lists the supported bases, which is appropriately concise for a straightforward conversion tool.
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?
Despite the tool's apparent simplicity, the description omits important details such as accepted string representations (e.g., '0x1A' vs '1A'), case sensitivity, and the exact return format. With no output schema and no annotation support, this is not fully sufficient for an agent to handle varied inputs correctly.
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 0%, so the description must compensate, but it only restates the base names already present as enum values. It does not clarify the 'value' parameter beyond calling it a number, even though the schema permits strings, leaving ambiguity about acceptable string formats.
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, 'Convert', and identifies the resource: a number among the four named bases. While it doesn't explicitly contrast with sibling conversion tools, the bases and operation are clearly stated and match the tool name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives, and no exclusions or prerequisite context. Although the sibling list contains several converters, no explicit routing to this one or away from others is provided.
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 bears full responsibility for behavioral disclosure. It mentions 'Compute' and 'exact great-circle', which implies a pure calculation, but it does not explicitly state that it has no side effects, requires no permissions, or what happens with invalid inputs. It also does not note any limitations (e.g., Earth's oblateness). The description adds algorithmic context but leaves safety and side-effect details implicit.
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 that conveys the core action and scope without any filler. It is appropriately concise for a tool of this simplicity, and every word contributes to understanding.
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, the description should specify the return value. It says 'in km/miles' but does not clarify whether it returns a single number in one of the units, both units, or allows the caller to choose. It also does not mention expected input units (e.g., decimal degrees) or any edge cases like anti-meridian crossings. For a four-parameter tool, this is incomplete.
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 0%, so the description must explain parameters. It says 'two lat/long coordinates' but does not map them to the four parameters (lat1, lon1, lat2, lon2), nor specify units (degrees vs radians) or the order of the coordinate pairs. The parameter names are self-explanatory, but the description adds no additional semantic value beyond what the property names already convey.
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 ('Compute'), the object (great-circle distance), and the context (two lat/long coordinates). It distinguishes itself from sibling tools like levenshtein_distance or calculate, which serve different purposes. The verb and resource are specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives, no exclusions, and no prerequisites. The description does not state 'Use this when you need distance between two geographic points', leaving the agent to infer its usage from the name and description. This is a minimal-viable hint but lacks explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It adds the useful trait that matching is deterministic and exact, but it does not disclose return format, similarity-ratio range, or case-sensitivity/normalization behavior. This is a partial disclosure, not a full one.
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?
One concise sentence, no filler, and the primary action is front-loaded. It earns its place despite being terse.
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?
For a two-string pure function, the core input and operation are clear, but there is no output schema and no description of what the computed result looks like. An agent could call it, but may not know how to interpret the similarity ratio or exact distance return value. This is adequate with visible gaps.
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 0%, so the description must compensate. It only says 'two strings,' which restates the type in the schema; it does not explain what a or b represent, length constraints, or expected encoding/format. The description adds minimal parameter meaning.
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 names a concrete operation: compute exact edit distance and similarity ratio between two strings. It is clear and uses the tool name meaningfully, but it does not explicitly distinguish this from sibling tools like diff_text.
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 given about when to choose this over diff_text or other string utilities, nor any exclusions or prerequisites. The 'deterministic fuzzy matching' phrase hints at a use case, but the agent is left to infer it.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the conversion operation, but does not mention whether the conversion is lossless, how errors (e.g., invalid input) are handled, whether comments are preserved (especially relevant for YAML/TOML), or whether the output is returned as a string. These are significant behavioral unknowns for a conversion tool.
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, concise sentence with no extraneous words. It front-loads the core action and the target formats, making it easy to scan and understand quickly.
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?
For a conversion tool with no output schema and no annotations, the description is notably incomplete. It omits details about return value (likely the converted string), error behavior, handling of format aliases (e.g., 'yml' vs 'yaml'), and any constraints on input size or complexity. An agent would need to experiment or rely on external knowledge to know what to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has zero parameter descriptions, so the description should compensate. It clarifies that the tool converts between the listed formats, which indirectly explains that 'from' and 'to' are the source and target formats (already evident from their enums). However, it does not explicitly state that 'input' is the config data to be converted, nor does it elaborate on the format of the input string (e.g., whether it must be a valid representation). The description adds some meaning but not enough to fully cover the low schema coverage.
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 'convert' and the resource 'config data' between three specific formats (JSON, YAML, TOML). This immediately distinguishes it from siblings like format_json (which likely formats or beautifies JSON) and convert_base (which converts numeric bases). The purpose is unambiguous.
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?
The description provides no guidance on when to use this tool versus alternatives, nor does it mention any prerequisites, limitations, or typical use cases. An agent must infer from the name and the mention of formats that this is the go-to for format conversion, but there is no explicit when-to-use or when-not-to-use guidance.
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 exist, so the description carries the full burden of behavioral disclosure. It states the operation but does not describe the output format, handling of invalid IPs, or potential side effects. This is a notable gap for a tool with zero 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 a single sentence that front-loads the action and resource, with zero wasted words. It is concise and easily parsed, fitting the tool's simplicity.
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?
For a one-parameter utility with full schema coverage, the description is adequate but missing return format details and edge-case behaviors (e.g., invalid IP input). Since no output schema exists, the description should ideally specify what the tool returns or errors, though the core operation is clear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides a descriptive example for the 'ip' parameter, achieving 100% schema description coverage. The tool description adds no additional parameter detail, so the baseline score of 3 applies per the rubric.
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 'check' and resource 'IPv4 address', and clarifies the outcome: private/reserved classification and range identification. It is distinct from siblings like calc_subnet, though it doesn't explicitly name them. The purpose is clear and actionable.
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 on when to use this tool versus alternatives is provided. However, the purpose is self-evident among the sibling utilities (e.g., calc_subnet for subnet math), so usage is implied. It lacks any when-not-to-use conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It usefully reveals that computation is exact and arbitrary-precision, which is meaningful. However, it omits edge-case behavior, such as what happens when r > n, and does not describe the return format.
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 efficient sentence with no filler. It front-loads the operation, names the mathematical concept, and adds the key precision guarantee in a compact way.
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?
This is a simple two-parameter pure function, so the description plus schema is mostly sufficient. However, there is no output schema to clarify return value format, and common edge cases such as r > n are not mentioned. It is adequate but not fully complete.
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 0%, so the description must compensate. The nCr notation implies that n is the total and r is the selection size, but the description does not explain parameter roles explicitly or mention constraints beyond the schema's minimums. Some meaning is added, but it is minimal.
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 operation ('Compute nCr') and the resource ('combinations'), and the mathematical notation makes the tool's purpose unambiguous. It does not explicitly differentiate from sibling tools like permutations, but the 'combinations' keyword provides strong disambiguation.
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 when-to-use guidance or exclusion of alternatives is provided. However, the phrase 'nCr (combinations)' implies usage when computing combinations, and 'exactly using arbitrary-precision integer math' suggests suitability for large values where floating-point approximations could be an issue.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It does add meaningful detail by promising correct handling of leap years and month boundaries, which is useful for date arithmetic. However, it does not disclose the return format, behavior on invalid dates, or the exact semantics of businessDays (e.g., weekday-only vs. holiday-aware).
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?
A single, front-loaded sentence communicates the operation, supported units, and a key correctness guarantee with no filler. Every phrase earns its place.
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 is adequate for a straightforward date arithmetic tool, and the required parameters are clear from the schema. However, there is no output schema and no mention of what the tool returns, so an agent must infer the return type and format. Some edge-case semantics, especially for businessDays, are left unexplained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes date and amount, and the enum lists the possible units. The description reinforces how the parameters combine (add/subtract amount of unit to/from date) and mentions businessDays, but it does not clarify the nuances of that unit. Coverage is moderate, so the description provides only marginal additional meaning.
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 names a precise operation — add or subtract — and a clear resource (a date), and it enumerates the exact supported units including businessDays. This makes it readily distinguishable from sibling tools like date_diff, convert_timestamp, and parse_date.
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 given about when to choose this tool over alternatives such as date_diff or convert_timezone. The context of sibling tools suggests a potential for confusion, but the description provides no exclusions or alternative routing.
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?
With no annotations, the description must handle behavioral disclosure entirely. It only states the high-level transformation, omitting critical details such as timezone handling (does it assume UTC or local?), error behavior for invalid strings, or the exact output structure (e.g., separate fields vs. a combined object). This leaves significant ambiguity for correct invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence stating the purpose directly with no filler. It is front-loaded with the core action and outputs, making it easy to scan. Efficiency is maximal without losing the essential meaning.
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?
For a single-parameter tool, the description covers the input adequately, but it fails to specify the output structure (no output schema exists) and does not address edge cases like invalid input or timezone interpretation. An agent would likely need to inspect behavior or make assumptions, leaving a notable gap for effective use.
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% – the 'date' parameter includes example formats in its description. The tool description adds no additional parameter-level detail, so it does not enhance what the schema already provides. Baseline 3 is appropriate since the schema carries the full semantic load.
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 ('Parse'), the resource ('a human/ISO date string'), and the two output forms ('ISO 8601 and Unix timestamp'). This is specific and distinguishable from siblings like convert_timestamp, which focuses on converting between timestamp formats rather than parsing human-readable dates.
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 intended use is implied: to convert date strings into two standard formats. However, there is no explicit guidance on when to choose this tool over alternatives (e.g., convert_timestamp, parse_cron), and no mention of exclusions or prerequisites. The agent must infer appropriateness based on the name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description is the only source of behavioral expectations. It does convey 'exact' arbitrary-precision arithmetic, but it omits return type, error behavior, and how invalid inputs like r > n are handled.
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 clear sentence that states the operation, the math formula, and the precision guarantee without unnecessary detail.
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 is self-contained enough for a straightforward computation, but without an output schema or edge-case guidance, an agent may not know how invalid inputs or very large results are returned.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The nPr notation implies that n is the total number and r is the selection size, and the schema enforces non-negative integers entering the API. However, the description does not explicitly define parameter meaning or expected input relationship.
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 states a specific operation ('Compute nPr') and names the mathematical domain. The formula nPr clearly identifies the tool's purpose and helps distinguish it from combination or factorial tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus related siblings like combinations, nor any mention of prerequisites or constraints such as n >= r.
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?
There are no annotations, so the description must carry the behavioral burden. It states the conversion action but does not disclose what the function returns—whether it returns one chosen format, all formats, or an object keyed by format. Given only one input parameter, this ambiguity is material to an agent invoking the tool.
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?
A single, well-structured sentence. It names the operation, the resource, and the supported formats without any redundancy or filler. The key information is front-loaded.
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 annotations and no output schema, the description carries the full burden for behavioral completeness. It fails to disclose what the tool returns—whether it outputs a single converted format, all three formats, or some structured object. The ambiguity of 'between' with a single input parameter leaves the agent guessing about the response shape.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema documents the color parameter with acceptable syntax examples (#rrggbb, hsl(), etc.). The description does not add parameter-level detail beyond saying 'color', but the schema already covers the accepted formats well, meeting the 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?
The description uses a specific verb ('Convert') with a clear resource ('a color') and the exact format scope ('hex, rgb(), and hsl()'). It is immediately distinguishable from sibling tools, which handle dates, timezones, configurations, and other non-color formats.
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 clearly implies when to use the tool: whenever a color in one of the three formats needs converting. It does not explicitly state exclusions or alternative tools, but there is no competing color tool among the siblings, so the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states a clear deterministic transformation ('encode text as base64'), but does not mention input encoding assumptions, character-set handling, or that the output is a base64 string—details that would aid trust and edge-case reasoning.
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 one concise sentence with the action front-loaded: 'Encode text as base64'. No filler words; every word earns its place.
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?
For a simple one-argument utility this may be enough, but with no annotations and no output schema it leaves the agent to assume the exact return format, character encoding behavior, and differences from sibling codecs like base64_decode.
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 only a bare 'text' string parameter with no description coverage. The description ties the parameter to the operation: the text to be encoded. This is minimal but sufficient for a single self-evident parameter, though it does not add format or example detail.
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 ('encode') and names the exact resource ('text') and output format ('base64'), so the tool's function is immediately unambiguous. It is clearly distinct from the sibling base64_decode.
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?
The description provides no guidance on when to choose this tool over alternatives, such as base64_decode or hash utilities. The intended use is only implied by the tool name and one-liner, leaving the agent to infer selection criteria.
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?
There are no annotations, so the description bears the full burden of behavioral disclosure. It lists what is computed, but it does not disclose whether stdev/variance use sample or population formulas, how percentiles are interpolated, or whether the input is left unmodified. This leaves meaningful ambiguity for a statistics tool.
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 main purpose and enumerates all expected outputs compactly. Every word adds value and there is no filler.
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?
For a single-parameter pure computation tool, the description is mostly sufficient: it states the input and the statistical outputs. However, with no output schema and no annotations, it does not specify the return structure or the stdev/variance convention, so an agent may make incorrect assumptions about the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes 'numbers' as an array of numbers with minItems 1, giving 100% schema description coverage. The description only confirms 'list of numbers' and adds no new parameter-level meaning, so the baseline score of 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 uses a specific verb ('Compute') and names the exact resource and outputs: mean, median, stdev, variance, min/max, and percentiles. This clearly distinguishes it from siblings like 'calculate' or 'count_tokens' without needing additional context.
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 phrase 'for a list of numbers' implies when the tool is appropriate, but there is no explicit when-to-use or when-not-to-use guidance and no comparison to alternatives. The usage context is implied rather than stated.
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 states the computation but does not disclose output format, error handling, or whether timezones are considered. For a calculation tool, this is a moderate gap.
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 with zero waste. It states the purpose and the specific output units efficiently.
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?
For a simple tool with full schema coverage and no output schema, the description covers the core purpose but omits output format details (e.g., whether it returns an object with separate fields or a combined string) and edge cases like invalid input. This leaves minor but notable 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 coverage is 100% with clear descriptions for both parameters ('First ISO 8601 date/datetime' and 'Second ISO 8601 date/datetime'). The description adds no extra semantic detail beyond restating that it takes two dates, so it meets the baseline for high schema coverage.
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 a specific verb 'Compute' and resource 'exact difference between two dates' with explicit units (days, business days, hours). It distinguishes itself from sibling tools like parse_date or date_add by focusing on difference calculation.
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 does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or conditions like timezone handling. However, the operation is self-evident for date comparison, so some guidance is implied but not articulated.
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?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It states the algorithms but does not describe the output format (e.g., hex string, encoding) or any side effects (none, but agents may not know it's pure). It does not mention that the function is deterministic or that it accepts only string text. This is a gap for a computation tool.
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, concise sentence that fronts the purpose and includes the list of algorithms. No fluff, every word contributes. It is appropriately sized for a simple tool.
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 tool has only 2 parameterseur, one required, and no output schema, the description is mostly complete for basic usage. However, it omits output format details (e.g., hex string) and does not clarify whether the 'algo' parameter has a default e.g., sha256? The anonymity of the algorithm list helps, but defining the return type would make it fully 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 the description must compensate. It explains the 'algo' parameter by listing valid algorithm names)Skip providing clarity on what 'algo' means. However, it does not explain the 'text' parameter beyond the name, though 'text' is self-explanatory. The description adds significant value for 'algo' but not for 'text', so a 4 is appropriate given the heavy schema gap.
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 (compute a hash digest) and the resource (text), and lists the supported algorithms. This distinguishes it from sibling tools like base64_encode or generate_uuid, which are all in the utility/computation domain. No 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 description does not explicitly state when to use this tool versus alternatives, but the context is clear: hashing text is a distinct operation. It doesn't mention when not to use it, but the tool's uniqueness among siblings makes it easy to infer. However, there is no guidance on algorithm choice or security considerations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It correctly states the tool handles only 5-field expressions and computes future run times, but it omits timezone assumptions, validation behavior, and whether 'next' is relative to the current moment. Adequate but minimal.
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?
A single, front-loaded sentence that states the action, input scope, and output in a compact way. There is no filler or redundant 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?
For a simple pure function with 2 documented parameters, the essential calling information is present. However, with no output schema, the return format (e.g., ISO strings, timestamps, date objects) is not specified, and timezone/validation behavior is unknown. These gaps are noticeable but not blocking.
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%, and the schema already documents both expression (with example) and count (with range and default). The description's 'N run times' adds no new parameter-level meaning, so the 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 uses specific verbs 'parse' and 'compute', names the exact resource (a 5-field cron expression), and defines the output (next N run times). It is unambiguous and clearly distinct from sibling utilities like parse_date or convert_timestamp.
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 when-to-use guidance or alternative tools are mentioned. The intended context is implied by the purpose, but the description does not state when to prefer it over other utilities. Since no sibling directly overlaps, this is a minor gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states the core transformation but does not disclose behavior on invalid base64 input, character encoding assumptions, or whether output is returned directly. For a simple decode operation, this is minimal but not misleading; it lacks edge-case 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?
A single sentence with no filler. The core purpose is front-loaded and every word earns its place. Ideal conciseness for a simple operation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one string parameter and no output schema, the description is sufficient for a standard decode operation. It does not cover error handling or output format details, but these are often implicit for such a well-known operation. The presence of siblings does not create confusion given the explicit decode action.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no description for the 'base64' parameter (0% coverage). The description indirectly clarifies that the parameter is the base64 string to decode, adding meaning beyond the schema's bare string type. However, it does not specify format examples or constraints, leaving some interpretation to the agent.
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 'Decode' and the resource 'base64 string to text'. It is specific and distinct from siblings like base64_encode, which performs the inverse operation. No ambiguity about the tool's function.
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 you have a base64 string and want the decoded text) but does not explicitly contrast with alternatives or state when not to use it. There is no mention of when to prefer base64_encode or other text utilities. The guidance is implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears the full responsibility for behavioral disclosure. It states the conversion and result, but it does not specify the output format, behavior on invalid timezones, or how input offsets are interpreted, leaving some uncertainty.
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 that contains no filler words and communicates the action, input, and result efficiently. It is well-structured and easy to parse.
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 only two fully documented parameters. The description indicates the return type ('formatted date+time string') but stops short of specifying the exact format (e.g., 'YYYY-MM-DD HH:MM:SS'). Given the lack of an output schema, a tiny bit more detail would make it fully self-contained, but this is sufficient for a straightforward conversion.
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%, with both parameters fully described in the input schema. The description adds no additional semantic meaning beyond 'date' and 'timeZone' as specified in the schema, so the baseline score of 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 states a specific verb ('convert'), resource ('a UTC/ISO date'), and outcome ('formatted date+time string for any IANA time zone'). This makes it unmistakably distinct from siblings like convert_timestamp or parse_date without needing to inspect schemas.
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 when to use the tool: whenever a UTC/ISO date needs to be presented in a particular IANA timezone. However, it does not explicitly name alternatives or state when not to use it, leaving the decision to the agent's familiarity with the sibling set.
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 responsibility for behavioral disclosure. The description explains the core transformation but does not mention error handling (e.g., invalid JSON), return format, or any side effects. For a pure transformation tool, this is minimal but not misleading; still, the lack of any behavioral nuance beyond the action itself leaves room for improvement.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with zero redundancy. It front-loads the primary action and covers both modes. There is no wasted wording, and the length is perfectly proportional to the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with only two parameters, one enum, and no output schema, the description is sufficient to enable correct invocation. The agent can infer that the tool returns the transformed string, and the sibling context shows a set of utilities where this tool's role is clear. Minor gaps such as explicit return format or error behavior are not critical given the tool's straightforward nature.
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% – both 'json' and 'mode' have descriptive entries, and 'mode' includes an enum. The tool description adds no additional meaning beyond what the schema already provides; it merely echoes the mode options. Since the schema fully documents parameters, a baseline score of 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 'Pretty-print or minify a JSON string' states a specific verb (pretty-print or minify) and a clear resource (JSON string). It unambiguously differentiates the tool from all sibling utilities, which focus on other data transformations (base64, hashing, date parsing, etc.). No ambiguity or tautology.
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 gives no explicit guidance on when to use this tool versus alternatives. While the tool is clearly a JSON formatting utility and no sibling overlaps, there is no mention of scenarios or exclusions. The intended use is implied by the tool's name and description, but explicit guidance is absent.
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. It does not mention that the tool returns multiple fields (network, broadcast, usable hosts, mask), whether it validates input, or how it handles invalid CIDRs. However, the description does indicate what outputs to expect, which is some behavioral context. Given no annotations, a score of 3 reflects the gap in error handling and return format details.
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, concise sentence that front-loads the tool's purpose and includes a concrete example. No wasted words.
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?
For a single-parameter tool with 100% schema coverage, the description is largely complete. However, there is no output schemaasi, and the description does not explain the format of the return value (e.g., whether it's a string or object, and how fields are named). An agent might need to invoke and inspect the result to understand the output. Overall, adequate but with a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage for the cidr parameter, including a description that explains what it is and gives an example. The description does add that the input is a CIDR, but this is already in the schema. Baseline 3 is therefore appropriate, as the schema does the heavy lifting.
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: to calculate subnet details (network, broadcast, usable hosts, mask) from a CIDR. It includes a concrete example, making the purpose unmistakable. It is distinct from sibling tools like check_ip_type, which inspects IPs for type, and the calculation tools, none of which are subnet-specific.
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 implies the tool is for subnet calculations and gives an example of input format. While it doesn't explicitly list when not to use it or mention alternatives, the sibling tools are functionally different (e.g., convert_unit, compute_statistics), so the intended usage is clear enough for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It communicates a non-destructive conversion operation, but it does not state the return format, failure behavior, or edge cases such as unsupported units or invalid values. This is adequate but not complete.
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?
A single dense sentence states the core action Fahrenheit and the reason to use the tool. It is free of redundancy and easy to scan, which helps an agent parse it quickly during tool selection.
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 is enough for a simple unit-conversion call if the schema fills the gaps, but there is no discussion of valid unit spellings, output shape, or failure modes. Given no annotations and no output schema, an agent only obtains partial context about how to call the tool robustly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already supplies unit exampleshare and example unit strings, and the description adds the supported categories. However, it is not clear whether strings like '3.5m' are accepted, whether compound units work, or how flexible unit names are, leaving some guesswork for the agent.
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 ('convert'), names the resource (a value), and narrows scope by listing length, weight, volume, and temperature. It naturally distinguishes this tool from siblings like convert_timezone or convert_base by stating the domain.
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 clearly implies when the tool should be used: whenever an agent needs to convert a value between physical units without doing error-prone arithmetic. It does not explicitly name alternatives or exclusions, but the category list makes the main usage context obvious.
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?
With no annotations provided, the description must fully disclose behavioral traits. It mentions the tokenizer, but does not state that the operation is read-only, what the return type is (e.g., an integer count), or any edge cases (e.g., handling of empty strings or invalid input). This leaves the agent guessing about the output format and safety profile.
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, front-loaded with the action, followed by a practical use case. No redundant words. It is efficient and easy to parse.
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?
For a simple one-parameter tool, the description is reasonably complete. However, it omits the return value and any side effects (though the action implies no mutation). It also doesn't state limitations (e.g., tokenizer applicability to specific model families). Given the absence of an output schema and annotations, these details would improve completeness.
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%, so the description must clarify the parameter. It does: 'text' is the input to count tokens. This adds meaning beyond the bare type definition. The description does not go into format or encoding, but for a single-string parameter, the clarity is adequate.
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 (count tokens in text) and the specific tokenizer (GPT-3.5/4 cl100k_base). It is distinct from sibling tools like calc_subnet or format_json, which serve different purposes. The resource (text) and operation are 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?
It gives a concrete use case: managing context budget before sending a large prompt. This tells the agent when to apply it. It doesn't explicitly mention when not to use it or list alternatives, but no sibling tool overlaps with token counting, so the guidance 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?
With no annotations provided, the description carries the full burden. It discloses the core behavior: line-by-line classification of added, removed, and unchanged lines. It does not specify output formatting or line-ending handling, but for a simple deterministic utility the behavioral disclosure is solid.
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 entire description is one efficient sentence, front-loaded with 'Line-by-line diff' and then the return categories. No unnecessary filler or repeated schema information.
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 two-string tool with a fully documented schema and no output schema, the description is sufficient: it tells the agent what the tool does and what the return represents. It does lack an example or output-marker detail, but that is minor for the complexity level.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema contains 100% parameter descriptions ('Original text' and 'Modified text'), so the baseline is 3. The description only refers to 'two text blocks' at a high level and adds no semantic detail beyond what the schema already states.
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 a specific verb and resource: 'Line-by-line diff of two text blocks' returning 'added/removed/unchanged lines'. This distinguishes it from siblings like levenshtein_distance or base64_encode, which operate differently, without needing to open the schema.
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 use case of comparing two text blocks line-by-line, but it gives no explicit instructions on when to reach for diff_text over a sibling such as levenshtein_distance, and does not mention exclusions or prerequisites. It is minimally viable but lacks concrete routing guidance.
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 carries the behavioral disclosure burden. It states that computation is exact and arbitrary-precision-less, and specifically warns about float64 overflow past ~18, which is useful behavioral context beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, immediately front-loaded with the operation 'Compute n! exactly', and adds only one clarifying detail about precision. It is easy to scan and parse.
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, deterministic math function with one input and no side effects, the description plus the schema is nearly complete. It could mention output size or the n=0 edge case, but nothing critical is missing for selection or invocation.
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 0%, so the description must supply meaning. It implies n is the factorial argument, but it does not mention edge cases such as 0! = 1 or the allowed range up to 10000, leaving the schema to carry those details.
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 ('Compute') and identifies the exact operation (n!) plus the mathematical domain (arbitrary-precision integers). This clearly separates it from general calculation tools and from combinatorial siblings like permutations and combinations.
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 purpose is clear enough to infer when factorial is wanted, and the note about avoiding float64 overflow hints at when exactness matters. However, it does not explicitly name alternatives or state when not to use this tool versus siblings like 'calculate' or 'combinations'.
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 are provided, so the description carries the full burden. It adds valuable behavioral context: exactness, arbitrary precision, and the supported operators/functions, which go beyond the schema's bare parameter description. It doesn't mention error handling or edge cases, but for a simple math 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?
Two sentences, front-loaded with the primary purpose and then a compact list of supported operations. Every word earns its place; no fluff.
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 single-parameter tool with no output schema, the description is complete enough: it states what it does, why it exists (fixing arithmetic errors), and what operations it supports. It could mention return format or error handling, but these are not critical for this simple utility.
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% – the schema already provides an example for the expression parameter. The description adds supported function context but doesn't substantially change parameter meaning beyond what the schema conveys, so the baseline of 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 tool evaluates arbitrary-precision math expressions exactly, with a specific verb ('evaluate') and resource ('math expression'). It also mentions fixing LLM arithmetic errors, which differentiates it from sibling tools like compute_statistics or factorial.
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 gives clear context for when to use it: whenever exact arithmetic is needed, especially for multi-digit numbers prone to LLM errors. It doesn't explicitly exclude alternatives, but the purpose is specific enough to route an agent correctly.
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 carries the transparency burden. It discloses accepted units (seconds or milliseconds), output format (ISO 8601), and the current-time behavior when omitted. It does not mention timezone handling or invalid inputs, but these are minor for this utility.
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?
One compact sentence that front-loads the conversion purpose, states accepted input units, and covers the optional-parameter behavior. No wasted 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?
For a one-parameter utility with no annotations and no output schema, the description covers purpose, parameter semantics, units, and the omitted-parameter case. Minor omissions like output timezone or error behavior are acceptable at this complexity level.
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?
Despite having no schema descriptions, the description adds the crucial unit detail (seconds or ms) and explains the effect of omitting the parameterage the optionality. It could also clarify whether a numeric string is accepted, but the core meaning is well supplied.
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 names the operation (convert Unix timestamp to ISO 8601) and the special no-argument case (return current time), making it easy to distinguish from siblings like convert_timezone and parse_date.
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 intended use is clear from the conversion action itself, but the description does not explicitly state when not to use it or mention alternatives, so guidance is only implicit.
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?
There are no annotations, so the description carries the behavioral burden itself. It meaningfully discloses that values are random, that UUID v4 is the format, and that count can produce more than one value. It does not describe the exact output shape, but for a pure generator this is enough to avoid most attribution errors.
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 short sentence with no filler. It front-loads the purpose, then defines the only meaningful variation (one or more). No sentence is wasted.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one optional parameter, no required inputs, and no nested schema, the description is nearly complete. It establishes the operation, randomness, format, and multiplicity. Its main gap is not explicitly stating the return representation for multi-UUID results, but this is a minor issue for a utility of this simplicity.
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 only one optional parameter exists. The description's 'one or more' phrase reinforces the count parameter, but adds little beyond what the schema already states about default and minimum. The schema is doing the heavy lifting, so the baseline of 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 names a specific action (generate), a specific resource (UUID v4 values), and a multiplicity (one or more). This clearly distinguishes the tool from all listed sibling utility/formula tools, none of which generate UUIDs.
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?
No explicit when/when-not guidance is given, but the usage context is unambiguous: call this tool when random UUID v4 values are needed. There are no close sibling alternatives requiring exclusionary guidance. The one clear context is sufficient for this simple utility.
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 carries the behavioral burden and does a good job by disclosing the primary limitation ('without verifying the signature') and the included expiry check. It does not mention error behavior or what happens with malformed or expired tokens, but the most important behavioral caveat is explicitly stated.
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 compact sentence that front-loads the verb and resource, then adds the critical caveat. There is no filler or redundant restatement of the parameter schema.
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 one-parameter utility tool, the description is largely sufficient: it identifies the input, the action, the included expiry check, and the key non-behavior. The main gap is the lack of any indication of return shape or error behavior, which would be helpful given there is no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema by indicating what decoding entails (header, payload, expiry check) and aligning with the token format. This raises it above the 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?
The description clearly states a specific verb ('Decode'), a specific resource (JWT), and the exact scope (header + payload + expiry check). It also explicitly carves out what it does not do ('without verifying the signature'), which distinguishes it from any verification-style tool and from all sibling utilities.
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 clearly communicates when to use the tool: for decoding and expiry checking, and explicitly warns that signature verification is not performed, implying it should not be used for security-critical verification. No alternative JWT-specific tool is named, but the guidance is still clear in context.
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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