Northern Forge MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a distinct purpose, from text utilities to product lookups. Even closely related functions like hash_text and base64_codec are clearly separated, leaving no ambiguity about which to select.
Naming Consistency4/5
All tool names are lowercase snake_case, creating a uniform style. Though not every name follows a strict verb_noun pattern, the mix of verb-first and noun-first names is predictable and easy to scan.
Tool Count2/5
With 30 tools, the server feels heavily overscoped. Many utilities such as now_iso, lorem_ipsum, and uuid_batch are trivial and unlikely to justify their place in the set, making it unwieldy for an agent to parse.
Completeness4/5
The product-related tools cover listing, retrieval, status, and analytics with no dead ends, though they lack creation or update capabilities. Each utility is self-contained and complete for its narrow purpose.
Average 3.8/5 across 30 of 30 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 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.
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This repository includes a glama.json configuration file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It implies a write operation ('Record') but does not mention side effects, validation, idempotency, permissions, or failure behavior. This is a significant gap for a mutation tool.
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 wasted words, and the key information is front-loaded. However, it is too terse to cover necessary behavioral details, so it loses a point for sacrificing substance.
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 4 parameters (including a nested object), no output schema, and no annotations, the description is far too sparse. It omits parameter meanings, expected behavior, error handling, and return values, making it insufficient for an agent to invoke the tool effectively.
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 provides example values for the 'type' parameter, leaving 'product_id', 'source', and 'meta' completely unexplained. No clarification of required vs. optional roles beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Record') and resource ('analytics event'), and lists concrete examples of event types. This effectively differentiates it from the sibling tools, which are unrelated utilities.
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 explicit guidance on when to use this tool versus alternatives. Usage is only implied by the verb and context, with no exclusions, prerequisites, or references to other tools.
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. It does disclose the conditional nature ('if configured'), but does not specify what happens when not configured (e.g., returns null, empty, or errors). No auth, rate limits, or other behavioral context is provided.
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 conveys all essential information without redundancy. It is front-loaded and every word contributes to the meaning.
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 one-parameter tool with no annotations or output schema, the description is too sparse. It lacks edge-case behavior (e.g., unconfigured products), parameter semantics, and any additional context an agent would need to invoke the tool reliably.
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 (product_id) with 0% description coverage. The tool description merely says 'for a product' but does not clarify whether product_id is a Stripe ID, internal ID, or its expected format. It fails to compensate for the schema's lack of documentation.
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 ('Return'), the resource ('known Stripe payment link URL for a product'), and the condition ('if configured'). It distinguishes itself from sibling tools like get_product and list_live_products by focusing specifically on the payment link.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied: use this tool when you need the Stripe payment link for a product. However, there is no explicit guidance on when not to use it or mention of alternatives, such as get_product for product details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that it is case-insensitive and uses a 'simple tokenizer', which provides some insight into its behavior. However, with no annotations provided, it doesn't mention whether it's read-only, what the return format looks like, or how punctuation and stop words are handled. This leaves significant behavioral aspects undisclosed.
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 core function. Every word contributes meaning, with no filler.
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?
The tool has no output schema, and the description does not specify the return format, ordering of frequencies, or whether a list of (word, count) pairs is produced. Given its simplicity, some information is still missing for a caller to fully understand 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 schema documents the 'limit' parameter with a default value, and the description implies that 'text' is the input string, adding minimal context. Since the description does not explicitly describe the 'text' parameter's format or constraints, it only partially compensates for the low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool computes top word frequencies from text, naming the resource (text) and the nature of analysis. It distinguishes from sibling text tools by focusing on word frequency rather than formatting or conversion. However, the verb is implicit, so it's not a perfect 5.
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 like diff_text or reading_time. It doesn't mention any exclusions or specific use cases. The context is only implied by the phrase 'word frequencies in text'.
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 exist, so the description carries the full burden. It does disclose the core behavior of producing multiple case variants, which is useful beyond the schema. However, it does not clarify the return structure (e.g., a single string, array, or object) or how edge cases like special characters are handled, leaving ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently lists all supported variants with zero wasted words. It earns its place.
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?
There is no output schema, so the description should explain what the tool returns. It does not specify whether the output is a string, an array of variants, or an object with named keys. This is a significant gap for an agent trying to predict the tool's behavior.
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 fully describes the single 'text' parameter with 100% coverage. The description adds nothing beyond the schema's 'Text to convert' definition, 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 uses a specific verb ('Convert') with a clear resource ('text') and enumerates the exact output formats (slug/kebab, snake_case, camelCase, PascalCase, CONSTANT_CASE, Title Case). This distinguishes it from sibling text utilities like hash_text or base64_codec.
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 other text utility siblings. There is no mention of when not to use it, no alternatives, and no context for choosing it over similar utilities.
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 disclose behavioral traits, but it only states the general conversion capability. It does not mention return format, edge cases (e.g., temperature offset conversions), or unit string normalization, leaving the agent with incomplete behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence and immediately states the tool's purpose. It is front-loaded and contains no redundant words, earning top marks for conciseness.
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?
The description lacks a list of supported unit abbreviations or an explanation of return values. Given that there is no output schema, the agent would need to guess valid unit strings (partial examples exist in the schema) and is not told what the converted result looks like, making the contextual information incomplete.
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 all three parameters (value, from, to) with full coverage, so the description adds no new parameter-level information. The category list (length, mass, etc.) gives high-level context but does not map to specific parameter behavior.
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 verb ('Convert') and resource ('numeric value between units'), and enumerates six unit categories, which distinguishes it from the sibling utility tools (e.g., base64_codec, json_to_ts). This gives the agent a precise understanding of what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a conversion between these unit types is needed, but it does not explicitly state when to use it over alternatives, nor does it provide exclusions. Since no sibling tool performs unit conversion, alternative guidance is unnecessary, but explicit 'use when' phrasing 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?
With no annotations, the description carries the disclosure burden. It tells the agent that the forge status is 'public' (no auth required) and lists the returned information, but does not mention potential side effects, data freshness, or behavior beyond read-only. For a status tool this is moderate 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?
The description is a single concise sentence that front-loads the core purpose ('Public forge status') and then lists the specific data surfaces. It is efficient and contains 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?
Given the tool has no parameters and no output schema, the description is reasonably complete: it names the resource (forge status) and enumerates all returned data items. However, it could further explain the format or purpose of these surfaces (e.g., what 'manifesto surfaces' means), leaving a small ambiguity.
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 empty. The baseline for a no-parameter tool is 4, and the description correctly omits parameter details since none exist. No parameter information is needed.
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 indicates the tool provides 'public forge status' and enumerates the specific data surfaces (hub URL, MCP endpoint, conversion API, product count, manifesto surfaces). It is distinct from sibling tools which address other utilities, but it lacks an explicit verb like 'get' or 'retrieve.'
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 on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or use cases, leaving the agent to infer that it should be used whenever forge status is needed.
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 description carries full burden. It discloses that it computes percent change and absolute delta, but doesn't specify return format (e.g., object vs. number), edge cases like division by zero when 'from' is 0, or rounding behavior.
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 efficient sentence that front-loads the core function and the additional delta output. No filler or redundant text.
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?
No output schema exists, and the description only hints at output via 'absolute delta.' It does not explain the return structure, number formatting, or how invalid inputs (e.g., from=0) are handled, leaving some uncertainty for an agent invoking the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% ('Starting value' and 'Ending value'), so description adds no new parameter semantics beyond mapping 'from/to' to 'old → new.' This is baseline for full 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?
Clear and specific: 'Percent change from old → new value (and absolute delta)' states the exact computation. Distinguishes from siblings like diff_text (text diff) and unit_convert (unit conversion).
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. Does not mention any exclusions or note that it's for numeric comparisons only.
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 disclosure. It mentions the 'offline' behavior, which is useful, but it does not specify output format, randomness, or other behavioral traits. Since this is a simple, non-destructive generator, the lack of additional detail is acceptable 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?
The description is a single, front-loaded sentence that maximizes information per word. Every element ('generate', 'placeholder lorem', 'paragraphs/sentences', 'offline') contributes to understanding, with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two optional parameters and no output schema, the description covers the essential purpose and the offline constraint. It could mention that the output is plain text, but that is largely implicit. Given the low complexity, the description is sufficiently complete for an agent to invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 100% of the parameter descriptions (sentences per paragraph and paragraph count with defaults). The description adds no extra parameter semantics beyond what the schema already provides, 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 clearly states the tool's function with a specific verb ('Generate') and resource ('placeholder lorem paragraphs/sentences'). It also adds a distinguishing detail ('offline'), which helps differentiate it from any online placeholder generators. 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?
No explicit guidance is provided on when to use this tool versus alternatives. The description implies use for placeholder text, but it does not mention any exclusions or alternative tools. The 'offline' note hints at one benefit but is not framed as a usage guideline.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses two behavioral traits: uniqueness (deduplication) and scheme filtering (http/https). However, it does not mention output format, handling of malformed URLs, or any edge-case behavior, leaving significant gaps.
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 short, front-loaded sentence. Every word earns its place, with no redundant fluff or repetition of schema details.
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 extraction tool with a single parameter and no output schema, the description covers the core purpose and input. However, it omits what the return value looks like (e.g., array of strings), which is a notable gap for a complete understanding.
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 has one required string parameter 'text' with no description (0% schema coverage). The description adds the meaning that this parameter is 'free text', which clarifies its nature beyond the schema. It does not elaborate on encoding or formatting, so the compensation is partial.
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 extracts unique http(s) URLs from free text. The verb 'extract' and resource 'unique http(s) URLs' are specific, and it distinguishes itself from sibling text tools like diff_text or regex_test.
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 need to pull URLs out of arbitrary text. However, it provides no explicit guidance on when to prefer this over alternatives (e.g., regex_test) and no exclusion criteria. The context 'free text' hints at the input type but does not elaborate.
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. It only states the action ('Get') without detailing return behavior, error handling, or permissions. For a read operation, the verb implies safety but does not explicitly disclose traits like 404 responses or full product object structure.
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 with no extraneous words, front-loading the purpose.
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 adequately states the tool's purpose and required identifier, but lacks any mention of return format or error behavior. Given the tool's simplicity, it is minimally viable but not fully complete.
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 provides 100% coverage of the parameter 'id' with the description 'product_id or slug'. The tool description repeats this information, adding no additional 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 the specific verb 'Get', identifies the resource as 'one product', and specifies the lookup key ('product_id or slug'). It clearly distinguishes from sibling 'list_live_products' which lists products.
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 usage when a single product is needed by ID or slug, providing clear context. However, it does not explicitly mention alternatives or when not to use, such as comparing with 'list_live_products'.
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 disclosure burden. It adds value by specifying the output fields and 'live' status, and 'Free' hints at no cost. However, it does not explicitly state read-only behavior, auth requirements, default limit behavior, or pagination details, which would enhance 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?
The description is two short, purposeful sentences. It front-loads the main action and resource, and every word earns its place with no redundant or vague content.
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 list tool with two optional parameters and no output schema, the description provides the resource and output fields, but it omits mention of the kind filter, limit/pagination behavior, and relationship to sibling tools like list_mcp_tools or get_product. This leaves some contextual gaps for an agent deciding to use it.
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 fully describes both parameters (kind and limit), so the baseline is 3. The description does not add any parameter-specific detail beyond the schema; the parenthetical list focuses on output fields, not inputs.
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 'List Northern Forge live products' with a specific verb and resource, and enumerates the returned fields (title, url, blurb, monetization). This distinguishes it from siblings like get_product (single item) and list_mcp_tools (a different resource type).
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 'Free core tool for agents' implies it is a basic, freely available listing operation, but there is no explicit guidance on when to choose this over alternatives such as get_product or popular_tools. No exclusions or when-not-to-use conditions are provided.
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 alone must convey behavior. It accurately states the transformation (encode/decode to/from UTF-8) but does not disclose edge cases such as invalid base64 input handling, behavior when both text and data are provided, or return format specifics. It is not misleading, but it lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tightly worded sentence that immediately conveys the tool's purpose. Every word contributes, and it is front-loaded with the primary verb 'Encode' and the resource 'base64'.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, the description covers the essential function, and the schema covers all parameters. Since there is no output schema, the description implicitly conveys the return value by explaining the transformation. It lacks explicit error-handling info but is otherwise complete for a straightforward 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%, so the parameters are fully documented in the schema. The description adds minimal extra meaning beyond the schema; it merely echoes encode/decode without elaborating on parameter interactions or formats. 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's function: encoding text to base64 and decoding base64 to UTF-8 text. It uses specific verbs and resources, and is easily distinguished from sibling utility tools (e.g., hash_text, html_escape) by its explicit base64 focus.
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 (for base64 encoding/decoding) but provides no explicit guidance on when to choose it over alternatives or mention any exclusions. The mode parameter is described in the schema, but the description itself does not offer usage direction beyond the core function.
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 burden of behavioral disclosure. It mentions the core operation and the entities handled, but does not disclose edge cases such as whether existing entities are double-escaped, behavior for non-listed characters, or the return format. This is minimal but not misleading.
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 no redundancy. It conveys the full purpose and key details efficiently.
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 text transformation tool, the description covers the essential purpose and entity set. However, it does not explicitly describe the return value (since there is no output schema), and the 'text' parameter lacks documentation. Still, it is largely complete for its 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 50% (only 'mode' has a description, 'text' does not). The description adds meaning by specifying the HTML entities involved, which implies the content of 'text', but it does not elaborate on parameter behavior beyond what the schema already states for 'mode'.
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 'Escape or unescape HTML entities (& < > " ')' clearly states the action (escape/unescape) and the target resource (HTML entities), listing specific characters. This distinguishes it from sibling utilities like base64_codec or hash_text, which perform different transformations.
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 need to escape or unescape HTML) but provides no explicit guidance on when to use this tool versus alternatives, nor any exclusions. It is adequate but lacks contextual 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 must disclose behavior itself. It states the input format and that it returns total grams and pounds, which is useful. However, it omits details like how qty is handled (multiplied?), defaults, or empty-array behavior, leaving some behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one focused sentence followed by a compact format spec and return statement. Every word contributes value; there is no redundancy or irrelevant content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool, the description covers key aspects: input structure and output units. It lacks a detailed return format (e.g., object keys) but given the simplicity and no output schema, it is adequately complete for an agent to 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 0%, but the description provides structure: 'items: [{name, grams, qty?}]', clarifying that items is an array of objects with those fields. It does not elaborate on the meaning of qty beyond optionality, so it only partially compensates for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a clear verb 'Sum' and specifies the resource 'gear pack items' and unit 'grams'. It accurately distinguishes this tool from siblings like unit_convert, as it sums weights rather than merely converting units.
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 (summing gear pack items in grams) but does not explicitly mention alternatives or exclusion cases. It provides a direct, straightforward use case without deeper guidance about when to choose this over other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It does state the outputs (word/char counts, minutes, mm:ss) and mentions WPM configurability, which is helpful. However, it does not mention assumptions about input text (e.g., plain text vs markup), edge cases like empty text, or the exact return format, leaving some gaps.
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 core purpose and immediately follows with the output types. No redundancy or 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?
The tool is simple, and the schema fully documents both parameters. The description explains the return values (word/char counts, minutes, mm:ss) since there is no output schema. Minor gaps remain, such as not indicating whether text length is limited or if HTML tags are stripped, but for a straightforward utility this is sufficient.
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% for both parameters, with text and wpm already described, including wpm's default of 200. The description adds little beyond the schema, only using 'blob of text' and 'default WPM,' which the schema already covers. Baseline 3 is appropriate since the schema handles 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 uses a specific verb ('Estimate') and resource ('a blob of text'), clearly stating the tool computes reading time with word/char counts, minutes, and mm:ss. This distinguishes it from all sibling tools, which are other utility functions like word_freq or diff_text, so 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for measuring reading time, but it does not explicitly state when to use it versus alternatives or provide exclusions. No guidance on when not to use it or how it compares to other text utilities (e.g., word_freq). Usage is implied 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, the description carries the full burden. It discloses that generation uses local crypto.randomUUID with a fallback, which is a useful implementation detail indicating a local, stateless operation. However, it does not mention error behavior (e.g., invalid count), nor explicitly confirm non-mutating side effects, leaving some transparency gaps.
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 of 15 words, front-loaded with the core purpose. The parenthetical about crypto.randomUUID/fallback is optional but relevant and does not bloat the text. Every word earns its place, making it an example of conciseness.
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 one-parameter utility with no output schema, the description is nearly complete. It tells what the tool does and implicitly what it returns (UUID v4 strings). The only missing detail is whether the output is always an array or a single string for count=1, but this is a minor ambiguity and the description is sufficient for practical 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?
The schema already fully describes the only parameter 'count' with default and max values (100% coverage). The description adds little beyond saying 'one or more', which is already implied. Since the schema does the heavy lifting, the description's contribution is redundant, meeting the baseline 3.
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 the specific action 'Generate one or more UUID v4 strings' with a clear resource (UUID v4). It distinguishes itself from sibling utility tools like hash_text or regex_test by being explicit about generating UUIDs. The implementation note (local crypto.randomUUID / fallback) adds relevant context without muddying the purpose.
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 this tool (whenever UUID v4 strings are needed) but does not provide explicit guidance on when not to use it or mention alternatives. No exclusions or caveats are given, so usage guidance is only implied, not 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 carries the full burden of disclosure. It discloses that results are 'approximate' and that it uses 'MVP solar math', which is important behavioral context. However, it does not mention potential limitations such as timezone handling, edge cases, or the exact output format, leaving some gaps.
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 main action ('Compute') and resource ('sunrise/sunset/golden-hour windows'). It includes valuable qualifiers ('approximate', 'no API key', 'MVP solar math') without any redundant wording, making every word earn 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?
Given the absence of an output schema, the description should explain return values, but it only implies outputs through the phrase 'sunrise/sunset/golden-hour windows'. It does not mention timezone or time format, which is relevant for astronomical times. The inclusion of 'approximate' and 'MVP' gives some context, but overall the description is not fully complete for a complex calculation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with all three parameters described (lat, lon, date). The description adds no additional meaning beyond what the schema already provides, simply restating 'for lat/lon/date'. Thus, 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 clearly states the tool computes approximate sunrise, sunset, and golden-hour windows for lat/lon/date using specific verbs and a specific resource. It distinguishes itself from sibling tools by focusing on solar calculations, which none of the siblings address. The 'MVP solar math' note further clarifies the scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by mentioning 'no API key', indicating this is a self-contained alternative to API-based services. It also qualifies the tool as 'MVP solar math', suggesting it is for approximate estimates. However, it does not explicitly name alternatives or state when not to use the tool, so it falls short of a 5.
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 full burden. It discloses that the tool handles optional quoted fields, which is a useful behavioral detail beyond the schema. However, it does not mention edge cases like empty fields, newlines, or error handling, leaving some transparency gaps for a transformation 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 front-loads the core function. Every word adds value, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter converter with no output schema, the description adequately specifies input format (CSV with optional quoted fields) and output (GitHub-flavored markdown table). It is complete enough for the tool's complexity, though it could mention handling of delimiters beyond the schema default.
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 covers both parameters (csv and delimiter) with 100% description coverage. The tool description does not add any additional semantic meaning beyond what the schema already provides, so it stays at 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 the tool's function: converting CSV text into a GitHub-flavored markdown table. It uses a specific verb ('convert') and names both the input (CSV text) and output (markdown table), distinguishing it from sibling conversion tools like json_to_ts.
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 this tool: whenever you have CSV text and want a markdown table. However, it does not explicitly mention alternatives or when not to use it, though no sibling tool appears to be a direct alternative.
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 burden. It explicitly discloses that signature verification is not performed and that it's for inspection only. However, it does not mention behavior for invalid tokens or the return format, which would increase 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?
The description is a single, concise sentence with no filler. It front-loads the key action and caveat efficiently.
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 decode tool with no annotations or output schema, the description covers the core function and main limitation. It could briefly mention what the decoded output looks like, but overall it is sufficiently complete.
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% as the token parameter is described as 'JWT string'. The description does not add extra semantics beyond what the schema already provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action (decode) and resource (JWT header + payload), and adds a critical caveat (no signature verify). This distinguishes it from sibling utility tools like base64_codec or hash_text.
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 'For inspection only' provides clear context for when to use the tool. It also implies not to use it for security verification, though it does not explicitly name alternative tools.
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. It discloses a key behavioral trait ('local, no LLM call') indicating no external call, but it doesn't describe side effects, output format, or determinism. For a transformation tool this is acceptable but incomplete, as it could mention whether the input is mutated or what exactly the output looks like.
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 entire description is a single, front-loaded sentence that gets straight to the point. There is zero waste, with all words contributing to the meaning.
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?
This is a simple utility tool with two parameters and no output schema. The description effectively covers the input (goal+notes), the operation (split), and the output (prompt variants for Claude/GPT/Grok). It could be more complete by specifying the number of variants or the exact format, but for a tool of this complexity, the essentials are covered.
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 0%, so the description must compensate. It mentions 'goal+notes blob' which implies the two parameters are combined into a single blob, giving some semantics beyond the bare schema. However, it doesn't elaborate on the individual meanings of 'goal' and 'notes' or clarify their role in the split, leaving partial clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Split') and clearly identifies the resource ('goal+notes blob') and the output ('Claude / GPT / Grok oriented prompt variants'). It distinguishes itself from sibling tools by naming the specific models and noting it's local with no LLM call.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: it's for splitting a goal+notes blob into model-specific prompt variants, with the key qualifier 'local, no LLM call' indicating when to use it (when you don't want an LLM call). However, it doesn't explicitly state alternatives or when not to use it, so it stops short of full exclusion guidance.
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. It discloses the return type (match count and up to 20 match slices), which is useful, but it does not explain behavior on no matches, invalid patterns, or side effects. It is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary purpose, and includes a concise summary of return behavior. Every word contributes meaning, with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description provides enough context for a simple regex tool by specifying the output format (count and slices). It lacks detail on the exact structure of slices, but for a basic test tool the description is sufficiently complete.
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 covers pattern and flags with descriptions, and the description adds the context that 'text' is the target string. Since coverage is 67% and the missing text description is somewhat self-evident, the description adds marginal value over the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool tests a JS-style regex against text, which is a specific verb+resource combination. It also distinguishes from siblings by being the only regex-related tool, 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 description implies the tool is used for testing JS-style regexes, and the context is clear given no sibling tools serve a similar purpose. However, it does not explicitly state when not to use it or mention alternatives, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. The verb 'Return' implies a read-only operation, and the description states what is returned (snippets). However, it does not explicitly mention side effects, auth needs, or error conditions. For a simple utility, this is adequate but not rich.
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 all essential information without unnecessary words. It is perfectly sized for the tool's simplicity.
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 tool with one optional parameter and no output schema, the description fully explains what the tool does and for which clients. No additional context is needed to invoke it correctly.
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 description covers 100% of the parameter 'client' with explicit allowed values and default. The description adds no extra parameter detail, 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 clearly states the tool returns ready-to-paste MCP install snippets for specific clients (Cursor, Claude Desktop, generic HTTP). This distinguishes it from sibling tools like list_mcp_tools, which list tools rather than provide config snippets.
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 context is clear: use this tool when you need install snippets for MCP clients. There are no explicit exclusions or named alternatives, but the description implicitly covers when to use it. The sibling tools are utilities, and this one stands apart as a config-snippet generator.
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 the full burden. It discloses a key behavioral trait: the source depends on conversion analytics availability and falls back to catalog seeds. This adds value beyond the schema, though it doesn't describe the exact output structure or any side effects, which are unlikely for a read-only ranking 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, front-loaded sentence that immediately communicates the core function. It is concise with no unnecessary words, earning a perfect score.
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 tool with a single optional parameter and no output schema, the description gives the essential purpose and fallback behavior. However, it does not specify the return format (e.g., array of product IDs vs. objects with scores), which is a gap given the lack of an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already fully documents the only parameter 'limit' with its default value (12). The description adds no further explanation about how limit affects results, but since schema coverage is 100%, 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 returns 'Hot/popular product ranks' based on conversion analytics with a fallback to catalog seeds. This specific verb+resource framing distinguishes it from sibling tools like list_live_products and get_product, which focus on listing or fetching individual products.
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 usage for popularity-based ranking and notes the 'when available' condition and fallback behavior, giving clear context for when to use it. However, it does not explicitly name alternatives or state when not to use it, though 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 discloses the input scope (5-field) and the nature of the output ('plain English'), but does not mention validation behavior, error handling, or the exact output format. This is minimal but adequate for a simple read-only 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?
The description is a single, well-structured sentence that front-loads the verb and resource, with no unnecessary words. It perfectly balances brevity and informativeness.
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 single-parameter utility with no output schema, the description is complete: it explains what the tool does, what input it expects, and what the output looks like ('plain English'). No additional context is necessary.
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 already documents the 'expr' parameter with an example, and the description adds the field order ('minute hour day-of-month month day-of-week'), enriching the parameter's meaning beyond the schema. Since schema coverage is high, this extra context boosts the score above baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb ('Explain') and resource ('5-field cron expression'), and it identifies the exact field order. This distinguishes it from any sibling tools, which are all unrelated 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 implies when to use the tool: whenever a 5-field cron expression needs translation to plain English. It does not explicitly discuss alternatives or exclusions, but no sibling tool serves a similar purpose, so the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It clearly states the tool returns unified-style +/- lines and counts, which describes the operational behavior. While it doesn't mention edge cases like whitespace handling, the pure-function nature makes this level of transparency 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?
The description is two sentences, with the main action front-loaded. The second sentence efficiently summarizes the return value. Every word earns its place, and there is no redundant or extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, the description fully covers what it does and what it returns. There is no output schema, but the return format is explicitly described. No missing side effects or prerequisites exist, making the description contextually complete.
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 with descriptions for both 'a' (Original text) and 'b' (Modified text). The description only refers to 'two text blobs' without adding any details beyond the schema, so no additional semantic value is provided. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Line-by-line diff of two text blobs,' which clearly identifies the verb (diff) and resource (two text blobs). It distinguishes this tool from siblings, none of which perform diffing, making the 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 description clearly implies when to use the tool: whenever two text blobs need comparison. It doesn't explicitly state alternatives or exclusions, but given the tool's unique role among siblings, this context is sufficient. The phrase 'Returns unified-style +/- lines' also sets expectations for the output.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the tool lists tools with schemas but does not explicitly state side effects or safety (e.g., read-only). The behavior is inherently non-destructive, but the description lacks any elaboration beyond the basic function.
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 a parenthetical clarification. Every word earns its place, and the key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple tool with no parameters and no output schema. The description fully explains that the tool lists server tools and schemas, which is complete for such a straightforward self-description capability.
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, so the baseline is 4. The description adds 'with schemas', which clarifies the output format, but no parameter semantics are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists the server's tools with their schemas, using the specific verb 'List' and identifying the resource. It distinguishes itself from sibling tools, which are individual utilities, by being a self-describing meta-tool.
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 'self-describe' implies using this tool to discover available tools and their schemas. It provides clear context for when to use it, though it does not explicitly mention alternatives or exclusions.
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 key behavioral traits: local execution ('Local crypto'), no network dependency, and hex output. It does not detail error behavior for invalid algorithm names, but for a simple hashing 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?
One concise sentence with all essential information front-loaded. No wasted words, and the security/network note is a valuable extra.
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-parameter tool with no output schema and no annotations, the description covers the primary purpose, algorithms, output format, and local behavior. It doesn't need to explain return values since a hash string is self-evident.
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 50% (algo has description, text does not). The description compensates by clarifying 'Hash a string' implies the text parameter is the input to hash, and the algorithm list matches the schema. This adds meaning beyond the schema for the text parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool hashes a string using sha256/sha1/md5, specifying the exact algorithms and output format (hex). This distinguishes it from sibling utilities like base64_codec or diff_text, which perform different transformations.
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 provides clear context: this is a local hashing operation with no network usage, which implies when to use it. However, it does not explicitly name alternative tools or state when not to use it, though the simple utility nature makes this less critical.
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 the full burden. It discloses that the tool runs locally (no network), does not rely on an LLM, and caps depth/size for safety. These are valuable behavioral traits beyond what the schema reveals.
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 clean sentences: first states the purpose, second adds safety and operational context. No fluff, front-loaded, every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple conversion tool, the description covers purpose, behavior, and safety. It doesn't detail error handling or exact return formatting, but the output is inferred as TypeScript interfaces, which is sufficient for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds nuance by mentioning the input can be a JSON string or object, which is not fully captured by the schema's 'string' type. It also hints at depth/size limits that affect parameter handling.
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 ('infer') and the resource ('JSON string or object') with a specific output (TypeScript interfaces). It distinguishes the tool from all sibling utilities, which are unrelated conversion/formatting tools.
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?
'Local, no LLM. Caps depth/size for agent safety.' provides context on when to use it (offline, deterministic, safe for agents) without explicit exclusions or alternative tool names. The usage context is clear, but no 'when not to use' is stated.
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?
Without annotations, the description carries the behavioral disclosure burden. It discloses accepted formats (#RGB or #RRGGBB) and that it reports AA/AAA pass for normal and large text, which is more than minimal. It does not mention error handling but that is not critical 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?
The description is a single, well-structured sentence that front-loads the core function and reports the output. No unnecessary words or repetition.
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-parameter tool, the description covers input format, the calculation performed, and the reported results. It does not explicitly state the return value structure, but that is not essential given the simplicity and lack of 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?
Although the schema already describes 'fg' and 'bg' as foreground/background hex colors, the description adds the supported formats (#RGB or #RRGGBB), providing extra semantic value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool computes the WCAG relative luminance contrast ratio between two hex colors and reports AA/AAA pass for normal and large text. It uses a specific verb and resource, making the purpose unmistakable and distinct from sibling tools.
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 its use case: checking color contrast for accessibility compliance. It does not explicitly mention alternatives or exclusions, but the context is clear enough for a tool of this simplicity.
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 bears the full burden of behavioral disclosure. It explicitly says the output formats (ISO-8601 UTC, unix seconds) and discloses a key limitation: the IANA timezone label is derived only from the offset, not a full timezone database. This is useful context that goes beyond the schema, though it does not explicitly state the tool has no side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that states the core purpose and key limitation without any filler. Every word contributes useful information, making it highly concise and well-structured.
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 simple stateless tool with one optional parameter and no output schema, the description fully covers the purpose, return formats, and a crucial limitation. The agent has enough information to invoke it correctly without needing further details.
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 already documents offset_minutes with an example, giving 100% coverage. The description adds meaning by linking this offset to the optional IANA timezone label, clarifying that the parameter controls the label output. This enriches the parameter semantics beyond the schema's simple 'local offset' description.
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 that the tool returns the current time in ISO-8601 UTC, unix seconds, and optionally an IANA timezone label. It is the only time-related tool among the siblings, so its purpose is unmistakable, even though it lacks an explicit verb like 'get' or 'fetch'.
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 when to use this tool: whenever the current time or timestamp is needed. The parenthetical 'offset only — no full tz DB' provides a caution about its limitations, effectively telling the agent not to use it for full timezone database lookups. However, it does not explicitly name alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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