Pylon MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose targeting specific resources and actions in the Pylon support system. For example, pylon_create_contact, pylon_create_issue, and pylon_create_webhook handle different entities, while pylon_get_issue, pylon_get_issue_messages, and pylon_search_issues provide distinct ways to access issue data without overlap.
Naming Consistency5/5All tools follow a consistent pylon_verb_noun naming pattern throughout the entire set. The structure is uniform with clear verbs (create, get, delete, search, update, snooze) followed by specific nouns (contact, issue, webhook, etc.), making the tool set predictable and easy to navigate.
Tool Count3/5With 29 tools, the count is borderline high for a support system server, potentially overwhelming for agents. While the tools cover comprehensive operations, a more streamlined set (e.g., 15-20 tools) might improve usability without sacrificing functionality, as some tools like pylon_get_issue and pylon_get_issue_messages could be consolidated.
Completeness5/5The tool set provides complete CRUD and lifecycle coverage for the Pylon support domain, including contacts, issues, teams, knowledge bases, webhooks, and more. It offers create, get, update, delete, and search operations across all major entities, with no apparent gaps that would hinder agent workflows in customer support management.
Average 3.7/5 across 29 of 29 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
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If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
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To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It states what data is returned (team names, member counts, specializations) but lacks critical behavioral details: whether results are paginated, sorted, filtered; authentication requirements; rate limits; error conditions; or whether this is a read-only operation (implied but not explicit).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficient sentences: first states purpose and examples, second specifies return data. No wasted words, though it could be slightly more structured (e.g., separating purpose from return format).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter read tool with no output schema, the description provides adequate basics (what it gets and what it returns) but misses important context: no output schema means the description should ideally detail return structure more precisely, and behavioral aspects like pagination/authentication are omitted.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, focusing instead on what the tool returns.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'all support teams from Pylon', with specific examples of team types (Technical, Billing, Sales). It distinguishes from siblings like 'pylon_get_team' (singular) by specifying 'all' teams, but doesn't explicitly contrast with other get tools like 'pylon_get_accounts' or 'pylon_get_users'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. While it implies retrieving all teams, it doesn't specify prerequisites, timing considerations, or contrast with similar tools like 'pylon_get_team' (singular) or 'pylon_get_users' (which might overlap with team members).
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 states this is a creation tool, implying mutation, but doesn't disclose behavioral traits like required permissions, whether tags are unique, rate limits, or what happens on duplicate names. This is a significant gap for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences that are front-loaded (purpose first, then usage). Every sentence adds value, though it could be slightly more concise by combining ideas.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a mutation tool with no annotations, no output schema, and 2 parameters, the description is minimally adequate. It covers purpose and basic usage but lacks details on behavior, permissions, or output, leaving gaps for an AI agent to invoke it correctly in complex scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters (name and color) with examples. The description doesn't add meaning beyond what the schema provides, such as constraints or usage context for parameters. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Create') and resource ('new tag'), specifying it's for categorizing issues and contacts. It distinguishes from siblings like pylon_create_contact or pylon_create_issue by focusing on tags, but doesn't explicitly differentiate from pylon_get_tags (a read operation).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for organizing support tickets, suggesting when to use it (to add categories for filtering). However, it doesn't provide explicit alternatives (e.g., when to use pylon_get_tags instead) or exclusions (e.g., not for modifying existing tags).
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 full burden for behavioral disclosure. It mentions what the tool returns ('contact details like name, email, company, and contact history'), which is helpful. However, it doesn't address important behavioral aspects like whether this is a read-only operation, authentication requirements, rate limits, pagination behavior, or what happens when no contacts match the search criteria.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that each serve a clear purpose: the first states the tool's function and context, the second describes the return values. There's no wasted language, and the information is front-loaded with the core purpose stated first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read operation with 2 parameters and 100% schema coverage but no output schema, the description provides basic context about what the tool does and what it returns. However, it lacks important details about behavioral characteristics (rate limits, authentication, error conditions) and doesn't clarify the relationship with the similar 'pylon_search_contacts' sibling tool, leaving gaps in complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions searching generally but doesn't provide additional context about parameter usage, interactions, or constraints beyond what the schema already specifies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'customer contacts from Pylon', specifying it's for finding customers who submitted support tickets or inquiries. It distinguishes from generic contact tools by mentioning the support context, but doesn't explicitly differentiate from sibling 'pylon_search_contacts' which appears to serve a similar search function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context about when to use it ('to find customers who have submitted support tickets or inquiries'), which implies usage for support-related contact lookup. However, it doesn't explicitly state when NOT to use it or mention the alternative 'pylon_search_contacts' sibling tool that appears to serve a similar search function, leaving some ambiguity about tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions what the tool returns (list of issues with details) but lacks critical behavioral information: whether this is a read-only operation, if it requires authentication, rate limits, pagination behavior (beyond the 'limit' parameter), or error conditions. The description doesn't contradict annotations (none exist), but provides minimal behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise (two sentences) and front-loaded with the core purpose. The second sentence adds useful context about use cases. No wasted words, though it could be slightly more structured by separating purpose from usage guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description provides basic purpose and usage but lacks completeness for a tool that returns data. It doesn't describe the return format in detail (beyond listing some fields), error handling, authentication requirements, or how results are ordered. For a read operation with 3 parameters, this is minimally adequate but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all three parameters (assignee, status, limit) with descriptions, examples, and constraints. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3 when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get support issues/tickets from Pylon' with specific details about what it returns (list with title, status, priority, assigned team member). It distinguishes from sibling 'pylon_get_issue' (singular) by implying this returns multiple issues, but doesn't explicitly differentiate from 'pylon_search_issues' which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some usage context: 'Use this to see your workload or find specific issues.' This implies when to use it (monitoring workload, finding issues) but doesn't explicitly state when to choose this tool versus alternatives like 'pylon_search_issues' or 'pylon_get_issue'. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the tool is for getting articles, implying a read-only operation, but doesn't disclose behavioral traits like pagination, rate limits, authentication needs, or error handling. For a tool with no annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences that are front-loaded with the core purpose. Each sentence adds value: the first states the action, and the second provides usage context. There is no wasted text, making it efficient, though it could be slightly more structured for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, 100% schema coverage) and no output schema, the description is minimally complete. It covers the purpose and basic usage but lacks details on behavior, output format, or error handling. With no annotations to fill gaps, it's adequate but leaves important contextual information unspecified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the single parameter (knowledge_base_id) with its type, description, and example. The description adds no additional parameter semantics beyond what's in the schema, such as format details or usage tips. Baseline 3 is appropriate when the schema handles all parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get help articles') and resource ('from a specific knowledge base'), making the purpose evident. It distinguishes from sibling tools like pylon_get_knowledge_bases by focusing on articles rather than bases, though it could be more explicit about the distinction. However, it doesn't fully specify the scope (e.g., all articles or filtered) beyond 'existing documentation'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage context ('to find existing documentation that might help resolve customer issues or to see what self-service content is available'), which suggests when to use it. It references pylon_get_knowledge_bases for obtaining the knowledge_base_id, offering some guidance. However, it lacks explicit alternatives (e.g., vs. search tools) or clear exclusions, leaving room for improvement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves all configured webhooks but doesn't mention important behavioral aspects like whether this requires authentication, if there are rate limits, what format the data returns in, or if it's paginated. The description adds some context about webhook functionality but lacks operational details needed for an agent to use it effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core functionality, and the second provides helpful context about webhooks. Every sentence adds value without redundancy. It's appropriately sized for a simple retrieval tool with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless retrieval tool with no output schema, the description provides adequate basic information about what the tool does and what webhooks are. However, without annotations and with no output schema, it lacks important operational details like authentication requirements, response format, or error handling. The context about webhook functionality helps, but more behavioral transparency would be needed for complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% description coverage, so there are no parameters to document. The description appropriately doesn't discuss parameters, which is correct for a parameterless tool. It focuses instead on explaining what the tool retrieves and providing context about webhooks.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get all configured webhooks') and resource ('in Pylon'), making the purpose immediately understandable. It distinguishes from sibling tools like pylon_create_webhook and pylon_delete_webhook by specifying retrieval rather than creation/deletion. However, it doesn't explicitly differentiate from other 'get' tools like pylon_get_accounts or pylon_get_issues beyond the 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 Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by explaining what webhooks do ('automatically send notifications to external systems when events occur'), which suggests this tool is for monitoring webhook configurations. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like pylon_get_issue or pylon_get_account, nor does it mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the tool is for searching, it doesn't describe key behaviors like whether this is a read-only operation, what permissions are required, if there are rate limits, how results are returned (e.g., pagination), or error handling. The description adds minimal behavioral context beyond the basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that efficiently convey purpose and usage. The first sentence states what the tool does, and the second provides context. There's no wasted text, though it could be slightly more structured by explicitly separating purpose from guidelines.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search function with one parameter) and lack of annotations or output schema, the description is minimally adequate. It covers the basic purpose and usage but lacks details on behavioral traits, result format, or error handling. For a search tool with no output schema, more context on what to expect would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'query' parameter well-documented in the schema itself. The description adds some semantic context by listing searchable fields (name, email, company, or other details), which aligns with but doesn't significantly expand upon the schema's examples. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search for customer contacts in Pylon by name, email, company, or other details.' This specifies the verb (search), resource (customer contacts), and scope (searchable fields). However, it doesn't explicitly differentiate from sibling tools like 'pylon_get_contacts' or 'pylon_search_users', which might offer similar functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: 'Use this to quickly find a specific customer when you need to view their information or create an issue for them.' This gives practical scenarios (viewing info, creating issues) but doesn't explicitly state when NOT to use it or name alternatives like 'pylon_get_contacts' for unfiltered lists.
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 full burden. It implies a write operation ('Create') but doesn't disclose behavioral traits like required permissions, whether duplicates are allowed, error handling, or what happens on success. This is a significant gap for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste: the first states the purpose, the second provides usage context. It's appropriately sized and front-loaded, with every sentence earning its place by adding value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a mutation tool with no annotations and no output schema, the description is incomplete. It covers purpose and usage but lacks behavioral details like permissions, side effects, or return values. However, it's adequate for basic understanding, aligning with a minimum viable score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters (email, name, portal_role) with details like format and examples. The description adds no parameter-specific information beyond what's in the schema, meeting the baseline of 3 when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create a new customer contact') and resource ('in Pylon'), with a specific purpose ('who will submit support requests or access your customer portal'). It distinguishes from siblings like 'pylon_create_issue' or 'pylon_create_team' by focusing on contacts, though it doesn't explicitly contrast with 'pylon_get_contacts' or 'pylon_search_contacts'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides clear context for when to use ('when adding a new customer who will submit support requests or access your customer portal'), which helps differentiate from other creation tools. However, it doesn't specify when NOT to use or explicitly name alternatives like 'pylon_update_issue' for existing contacts, keeping it from a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that issues are for tracking and resolution, which implies persistence and workflow, but lacks details on permissions required, rate limits, whether creation is idempotent, or what happens on success/failure. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, consisting of two concise sentences that directly state the tool's purpose and usage context without any wasted words. Every sentence earns its place by providing essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a mutation operation with 5 parameters) and the absence of both annotations and an output schema, the description is moderately complete. It covers the basic purpose and usage but lacks details on behavioral aspects like error handling or return values, which are important for a creation tool without structured output documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, meaning all parameters are well-documented in the input schema itself. The description does not add any additional meaning or context about the parameters beyond what's already in the schema, such as explaining relationships between fields or usage tips. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does 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 ('Create') and resource ('new support issue/ticket in Pylon'), and distinguishes it from siblings by specifying it's for logging customer problems, bug reports, or feature requests. This differentiates it from other create tools like pylon_create_contact or pylon_create_team.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use this tool ('to log customer problems, bug reports, or feature requests that need to be tracked and resolved'), which helps guide usage. However, it doesn't explicitly mention when not to use it or name specific alternatives among the sibling tools, such as pylon_update_issue for modifying existing issues.
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 full burden for behavioral disclosure. While it implies a write operation ('Add'), it doesn't disclose important traits like authentication requirements, rate limits, whether messages are editable/deletable, or how the system handles different message types (customer vs internal). The description mentions message purposes but lacks operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficient sentences that front-load the core purpose and follow with usage contexts. Every word earns its place with no redundancy or unnecessary elaboration. The structure moves from general action to specific applications.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a write operation with no annotations and no output schema, the description is adequate but incomplete. It covers the what and why well, but lacks information about behavioral consequences, error conditions, or response format. Given the mutation nature and absence of structured safety hints, more disclosure would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters. The description adds no additional parameter information beyond what's in the schema. This meets the baseline of 3 when schema coverage is high, but doesn't provide extra value like format examples or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Add a new message/reply') and target resource ('to a support issue conversation'), distinguishing it from siblings like pylon_create_issue (creates issues) and pylon_get_issue_messages (reads messages). It goes beyond the tool name by specifying the conversational context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('to respond to customers, add internal notes, or provide updates on issue progress'), giving practical examples. However, it doesn't explicitly state when NOT to use it or mention alternatives like pylon_update_issue for other issue modifications.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it indicates this is a creation tool, it lacks details on permissions required, whether the article is published immediately, rate limits, error handling, or what happens on success (e.g., returns an article ID). This is a significant gap for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and followed by usage context. Every sentence earns its place with no wasted words, making it efficient and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that this is a mutation tool with no annotations and no output schema, the description is incomplete. It covers the purpose and usage context adequately but lacks behavioral details (e.g., side effects, response format) and does not compensate for the absence of structured fields, leaving gaps for an AI agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description does not add any additional meaning or context beyond what the schema provides (e.g., it doesn't explain parameter interdependencies or usage tips). Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Create') and resource ('new help article in a knowledge base'), distinguishing it from siblings like pylon_create_contact or pylon_create_issue. It specifies the purpose is for adding documentation, FAQs, or troubleshooting guides for customer self-service support, making it highly specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use this tool ('to add new documentation, FAQs, or troubleshooting guides that customers can access for self-service support'), but it does not explicitly mention when not to use it or name alternatives. For example, it doesn't compare to pylon_create_issue for support tickets or pylon_get_knowledge_base_articles for retrieval.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. While it mentions the tool creates a new team, it doesn't disclose important behavioral traits like required permissions, whether this action is reversible, rate limits, or what happens if duplicate team names are used. The description adds some context about team organization but lacks critical operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core purpose, and the second provides usage context with concrete examples. Every sentence adds value without redundancy, making it appropriately sized and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a creation tool with no annotations and no output schema, the description provides adequate purpose and usage context but lacks important behavioral details about permissions, side effects, and response format. The 100% schema coverage helps, but the description should compensate more for the missing annotations and output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing complete documentation of all three parameters. The description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline of 3. No additional semantic context is provided for the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Create a new support team') and resource ('in Pylon'), with explicit examples of team specializations that distinguish it from sibling tools like pylon_create_contact or pylon_create_issue. It goes beyond the tool name by explaining the organizational purpose of teams.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('to organize support agents into specialized groups for handling different types of customer issues') with helpful examples. However, it doesn't explicitly state when NOT to use it or mention specific alternatives among the sibling tools, such as when to use pylon_get_teams instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While it states this is a creation tool ('Create a new ticket submission form'), it doesn't disclose important behavioral traits such as required permissions, whether this operation is idempotent, what happens on failure, or what the expected response format might be. For a mutation tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences with zero waste. The first sentence states the core purpose, and the second sentence provides specific usage context with concrete examples. Every sentence earns its place by adding distinct value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a creation/mutation tool with no annotations and no output schema, the description provides adequate purpose and usage context but lacks important behavioral information about permissions, error handling, and response format. The 100% schema coverage helps with parameter understanding, but the overall context remains incomplete for a tool that modifies system state.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, providing detailed documentation for all three parameters. The description adds no additional parameter semantics beyond what's already in the schema. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline score is 3 even with no parameter information in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Create a new ticket submission form') and resource ('for customers'), with explicit differentiation from sibling tools like 'pylon_create_issue' or 'pylon_create_contact' by focusing on form customization for support requests. It provides concrete examples of form types (bug reports, feature requests, billing questions) that establish its distinct purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool ('to customize what information customers provide when creating different types of support requests') and provides examples of those request types. However, it doesn't mention when NOT to use it or name specific alternatives among the sibling tools, such as when to use 'pylon_create_issue' directly instead of creating a form.
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 full burden but only states the basic purpose. It doesn't disclose important behavioral traits: whether this requires specific permissions, what happens on duplicate creation, rate limits, authentication needs, error conditions, or what the response contains. For a creation tool with zero annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste. First sentence states the core purpose clearly, second provides practical usage context with specific examples. Every word earns its place, and the structure is front-loaded with the main action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a creation tool with no annotations and no output schema, the description provides adequate basic purpose but lacks completeness. It doesn't explain what happens after creation, what the return value contains, error handling, or important constraints. The 100% schema coverage helps, but behavioral aspects remain undocumented.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions integration examples but doesn't explain parameter relationships or usage nuances. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Create a new webhook') and resource ('webhook'), with explicit purpose ('to automatically notify external systems when events occur in Pylon'). It distinguishes from siblings like pylon_delete_webhook by focusing on creation rather than deletion, and from other create tools by specifying webhook functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('to integrate with Slack, Discord, email systems, or custom applications'), giving practical integration examples. However, it doesn't explicitly state when NOT to use it or mention alternatives like pylon_get_webhooks for viewing existing webhooks, though the sibling list shows related 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 of behavioral disclosure. It states this is a deletion operation, implying it's destructive, but doesn't specify whether deletion is permanent, requires specific permissions, or has side effects like stopping notifications immediately. For a mutation tool with zero annotation coverage, this leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with zero waste: the first states the purpose, and the second provides usage context. It's front-loaded with the core action and efficiently conveys necessary information without redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a destructive operation with no annotations and no output schema), the description is adequate but incomplete. It covers the purpose and usage context but lacks details on behavioral aspects like permanence, permissions, or response format. For a deletion tool, more transparency would be beneficial to ensure safe and correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already fully documents the single parameter (webhook_id). The description adds minimal value beyond the schema by implying the parameter is used to identify the webhook to delete, but doesn't provide additional syntax, format, or usage details. This meets the baseline of 3 when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Delete an existing webhook') and resource ('webhook'), distinguishing it from sibling tools like pylon_create_webhook or pylon_get_webhooks. It uses precise language that directly conveys the tool's function without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit context for when to use this tool ('when removing integrations or cleaning up unused webhooks'), which helps the agent understand appropriate scenarios. However, it doesn't explicitly mention when NOT to use it or name alternatives, such as using pylon_get_webhooks first to identify webhook IDs, which would elevate it to a 5.
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 full burden for behavioral disclosure. It states the tool returns detailed account information but doesn't mention authentication requirements, rate limits, error conditions, or whether this is a read-only operation (though 'Get' implies it). The description provides basic behavioral context but lacks important operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first states the core purpose and target, the second enumerates the specific data returned. Every element serves a clear purpose with zero wasted words, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read operation with no output schema, the description provides adequate context about what data is returned. However, without annotations covering safety/behavioral aspects and no output schema, it could benefit from more detail about response format, error handling, or authentication requirements given it accesses customer data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents the single account_id parameter with examples and sourcing guidance. The description doesn't add any parameter-specific information beyond what's in the schema, meeting the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get detailed information'), target resource ('about a specific customer account'), and scope of returned data ('company details, subscription info, billing status, and associated contacts and issues'). It distinguishes from sibling pylon_get_accounts by specifying retrieval of a single account rather than a list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'specific customer account' and the input schema provides guidance on obtaining the account_id from pylon_get_accounts. However, it doesn't explicitly state when to use this tool versus alternatives like pylon_get_contacts or pylon_get_issues for related data.
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. It discloses that the tool returns a list with names and article counts, which is useful behavioral context beyond the input schema. However, it lacks details on permissions, rate limits, pagination, or error handling that would be important for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences that efficiently convey the tool's purpose and output. Every word earns its place, with no redundancy or fluff, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is reasonably complete. It explains what knowledge bases are and what the return includes, though it could benefit from more behavioral details like response format or limitations to fully compensate for the lack of annotations and output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description appropriately adds no parameter information, maintaining focus on the tool's purpose and output. Baseline is 4 for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('all knowledge bases from Pylon'), with additional context about what knowledge bases contain (help articles, FAQs, documentation). It distinguishes from sibling tools like 'pylon_get_knowledge_base_articles' by focusing on the bases themselves rather than their content, though not explicitly named.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied by the description's focus on retrieving knowledge bases for customer access, but there's no explicit guidance on when to use this tool versus alternatives like 'pylon_get_knowledge_base_articles' or other get tools. It provides basic context without exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states what the tool does but doesn't disclose behavioral traits such as whether it's read-only, requires authentication, has rate limits, or what the return format looks like. For a tool with no annotations, this leaves significant gaps in understanding its operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and followed by clarifying examples. Every sentence earns its place by adding value without redundancy, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but incomplete. It explains what forms are but doesn't cover behavioral aspects or output details, which are needed for full contextual understanding despite the low complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description adds no parameter information, which is acceptable since there are no parameters to document, and it doesn't need to compensate for any gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'all ticket submission forms available to customers', with specific examples (bug report form, billing inquiry form) that distinguish it from sibling tools like pylon_get_issues or pylon_get_contacts. It precisely defines what forms are in this context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by mentioning 'available to customers' and the purpose of forms, but doesn't explicitly state when to use this tool versus alternatives like pylon_get_issues or pylon_create_ticket_form. It provides context but lacks explicit guidance on exclusions or comparisons.
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 mentions searching by name, email, or department, but lacks details on behavioral traits such as permissions needed, rate limits, pagination, or what the search returns (e.g., partial matches, case sensitivity). This leaves significant gaps for a search tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, with two sentences that efficiently convey purpose and usage. Every sentence adds value without redundancy, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search function with one parameter), no annotations, and no output schema, the description is adequate but incomplete. It covers the basic purpose and usage but lacks details on behavioral aspects and output format, which are important for effective tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the 'query' parameter well-documented in the schema. The description adds minimal value by restating searchable fields (name, email, department) without providing additional semantics like search logic or examples beyond the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search for team members and support agents in Pylon') and distinguishes it from siblings like 'pylon_get_users' by specifying search functionality. It identifies the resource ('team members and support agents') and the context ('Pylon'), making the purpose explicit and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('when assigning issues or checking availability'), which helps guide usage. However, it does not explicitly state when not to use it or name alternatives (e.g., 'pylon_get_users' for listing all users without search), missing full differentiation.
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 behavioral disclosure. It describes the action ('temporarily hide an issue') and its reversible nature ('until a future date/time'), which is useful. However, it doesn't cover important behavioral aspects like permissions required, whether the issue remains accessible while snoozed, error conditions, or rate limits. The description adds some value but leaves gaps 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the core functionality, and the second provides usage context with examples. Every sentence earns its place with zero waste, making it highly efficient and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a mutation with 2 parameters), no annotations, and no output schema, the description is somewhat complete but has gaps. It explains the purpose and usage well but lacks details on behavioral aspects like permissions, side effects, or return values. For a mutation tool without annotations, it should do more to be fully comprehensive, but it meets minimum viability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for both parameters (issue_id and until). The description doesn't add any parameter-specific details beyond what's in the schema, such as format nuances or constraints. According to the rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does 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 ('hide') and resource ('issue'), and specifies the temporal aspect ('until a future date/time'). It distinguishes from sibling tools like pylon_update_issue by focusing on temporary hiding rather than modification, though it doesn't explicitly name alternatives. This is clear but lacks explicit sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('issues that cannot be worked on now but need follow-up later') with concrete examples (e.g., waiting for customer response). It implies usage scenarios but doesn't explicitly state when not to use it or name specific alternatives like pylon_update_issue for permanent changes, which keeps it from a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the tool updates an existing issue, it fails to disclose critical behavioral traits such as required permissions, whether changes are reversible, rate limits, or what happens to unspecified fields. For a mutation tool with zero annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with two sentences that efficiently convey the tool's purpose and usage. Every sentence earns its place by providing essential information without redundancy or unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (mutation operation with 6 parameters) and lack of annotations and output schema, the description is incomplete. It adequately covers the purpose and basic usage but fails to address behavioral aspects like permissions, side effects, or response format, leaving gaps for an AI agent to operate safely and effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 6 parameters with examples and constraints. The description adds marginal value by listing some parameter purposes (e.g., 'change status', 'reassign to different team members'), but doesn't provide additional syntax or format details beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('Update') and resource ('existing support issue/ticket'), and distinguishes it from siblings like pylon_create_issue (for creation) and pylon_get_issue (for retrieval). It explicitly lists key operations like changing status, reassigning, updating priority, or modifying details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('as you work on the issue') and implies usage for updating existing issues, distinguishing it from creation tools. However, it lacks explicit exclusions or alternatives (e.g., when not to use vs. pylon_snooze_issue or pylon_search_issues), which prevents a perfect score.
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 that the tool returns conversation history with timestamps and sender info, which is useful behavioral context. However, it lacks details on potential limitations like pagination, rate limits, authentication requirements, or error conditions, leaving gaps for a read operation with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional context in a second sentence. Every sentence adds value (e.g., specifying return content and usage guidance), with no redundant or wasted words, making it efficiently structured and appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (a read operation with one parameter) and no annotations or output schema, the description is adequate but incomplete. It explains what the tool does and its purpose, but lacks details on output format (beyond high-level content), error handling, or system constraints, which could hinder an agent's ability to use it effectively without trial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage (the issue_id parameter is fully documented), so the baseline is 3. The description does not add any parameter-specific information beyond what the schema provides, such as format constraints or examples, but it doesn't need to compensate for gaps since schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get the conversation history') and resource ('for a specific support issue'), distinguishing it from sibling tools like pylon_get_issue (which likely returns issue metadata) or pylon_search_issues (which searches across issues). It explicitly mentions the content returned ('all messages between customer and support team, including timestamps and sender information'), 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 Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('to understand the context and progress of an issue'), which implicitly suggests it's for detailed message retrieval rather than high-level issue overviews. However, it does not explicitly state when not to use it or name specific alternatives among the siblings (e.g., pylon_get_issue for basic issue details), which prevents a perfect score.
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 describes the operation as a read ('Get') and outlines what data is returned ('account details like company name, subscription level, and contact information'), which is helpful. However, it lacks details on behavioral traits such as pagination, rate limits, authentication requirements, or error handling, which are important for a tool that fetches 'all' accounts.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences that are front-loaded with the core purpose and follow with additional context. Every sentence adds value: the first defines the action and resource, and the second explains what accounts are and what details are returned. There is no wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a read operation with no parameters but fetching 'all' accounts), no annotations, and no output schema, the description is adequate but incomplete. It covers the purpose and return data types but misses important context like response format, pagination, or potential limitations (e.g., large result sets). For a tool with no structured output, more detail on the return structure would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameters need documentation. The description doesn't add parameter details, which is fine here, but it could have mentioned if there are implicit parameters (e.g., sorting or filtering options). Baseline is 4 for 0 parameters, as it doesn't need to compensate for gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('all customer accounts from Pylon'), specifies the scope ('all'), and distinguishes it from the sibling tool 'pylon_get_account' (singular) by emphasizing it returns multiple accounts. It also explains what accounts represent ('companies or organizations that use your service').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by stating it retrieves 'all customer accounts,' suggesting it's for bulk retrieval rather than looking up a specific account (which would use 'pylon_get_account'). However, it doesn't explicitly state when not to use it or mention alternatives like searching or filtering, which are available in sibling tools like 'pylon_search_contacts' or 'pylon_search_issues'.
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 full burden. It discloses that it returns full issue information with listed fields, which is helpful. But it omits behavioral traits like error handling, permissions needed, or rate limits, leaving gaps for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and followed by specifics on returned data. Every sentence adds value without waste, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description provides basic purpose and return fields, but lacks details on output format, error cases, or operational constraints. For a tool with 1 parameter and high schema coverage, it's adequate but has clear gaps in behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the single parameter 'issue_id' with examples. The description adds no extra param details, but with 0 parameters beyond the one covered, baseline is 4 as it doesn't need to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'complete details of a specific support issue/ticket', specifying it returns full information including title, description, status, etc. It distinguishes from siblings like pylon_get_issues (list) and pylon_search_issues (search).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving a specific issue by ID, and the input schema suggests getting the ID from pylon_get_issues or pylon_search_issues, providing context. However, it lacks explicit when-not-to-use guidance or alternatives for similar operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the return content (user profiles with specific fields) and that it retrieves all users, but doesn't mention behavioral aspects like pagination, rate limits, authentication requirements, or whether this is a read-only operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste: the first states the purpose and scope, the second describes the return value. Every word earns its place, and the description is appropriately sized for a simple retrieval tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a retrieval tool with no parameters and no output schema, the description adequately explains what it returns. However, without annotations or output schema, it could benefit from more behavioral context (like whether it's paginated or requires specific permissions) to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description doesn't need to explain parameters, and appropriately focuses on what the tool does rather than parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get all team members and support agents') and resource ('in your Pylon workspace'), with explicit scope ('all'). It distinguishes from siblings like 'pylon_search_users' by indicating this returns all users without filtering.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving comprehensive user profiles, but doesn't explicitly state when to use this vs. alternatives like 'pylon_search_users' or 'pylon_get_me'. However, the context is clear for fetching all users rather than filtered subsets.
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 describes the search behavior and scope but lacks details on permissions, rate limits, pagination, or response format. It doesn't contradict annotations, but for a search tool with no annotation coverage, more behavioral context would be helpful.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose followed by usage guidelines. Zero wasted words, efficiently communicates essential information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with no annotations and no output schema, the description provides adequate purpose and usage but lacks details on behavioral traits like result format, pagination, or error handling. It's minimally viable but has clear gaps in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description mentions searchable fields ('keywords, customer name, or issue content') which aligns with the schema but doesn't add significant semantic value beyond it. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Search for') and resource ('support issues/tickets in Pylon'), specifying searchable fields ('keywords, customer name, or issue content'). It distinguishes from sibling tools like pylon_get_issues by focusing on search functionality rather than general retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides when-to-use guidance: 'Use this to find related issues, check for duplicates, or research similar problems.' This gives clear context for selecting this tool over alternatives like pylon_get_issues or pylon_search_contacts.
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. It discloses that this is a read operation ('Get') that returns user profile information, which is helpful. However, it doesn't mention potential behavioral aspects like authentication requirements (though implied by 'verify your authentication'), rate limits, error conditions, or response format details beyond listing fields.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences that are front-loaded with the core purpose, followed by the specific use case. Every word earns its place, with no redundant or unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is reasonably complete. It explains what the tool does and when to use it. However, without an output schema, it could benefit from more detail about the return structure (e.g., format of permissions field) or error cases, though the current information is adequate for basic use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, maintaining focus on the tool's purpose and usage. This meets the baseline for tools with no parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'current user information from Pylon', specifying it returns user profile details like name, email, role, and permissions. It distinguishes itself from sibling tools like pylon_get_users (which likely lists multiple users) by focusing on the authenticated user's own information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: 'to verify your authentication and see what access level you have.' It doesn't explicitly state when not to use it or name alternatives, but the context strongly implies this is for self-identification and permission checking rather than retrieving other users' data.
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 describes what the tool does (retrieves tags) and their purpose, but lacks details on behavioral traits such as permissions required, rate limits, response format, or whether it's a read-only operation. The description is accurate but minimal in behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by a clarifying example in the second. Both sentences earn their place by adding specific value without redundancy, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is mostly complete for a simple retrieval tool. It explains what tags are and their use cases, but could improve by mentioning the return format or any limitations, though not strictly necessary given the context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by explaining the semantics of tags (e.g., for categorizing issues and contacts) and providing examples, which enhances understanding beyond the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'all available tags', specifying their purpose for categorizing issues and contacts. It distinguishes from sibling tools like pylon_create_tag by focusing on retrieval rather than creation, and provides concrete examples (e.g., 'bug', 'feature-request') to clarify the nature of tags.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for organizing and filtering support tickets by topic, urgency, or type, which gives clear context. However, it does not explicitly state when to use this tool versus alternatives like pylon_get_issues or pylon_search_issues, nor does it provide exclusions or prerequisites for usage.
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. It discloses that the tool returns detailed information (team members, roles, workload, metrics), which is useful behavioral context. However, it does not mention potential limitations like rate limits, authentication needs, or error handling, leaving gaps in transparency for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: the first states the purpose and resource, and the second lists the returned details. Every sentence adds value without redundancy, and it is front-loaded with the core action. No wasted words, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a read operation with one parameter), no annotations, and no output schema, the description is reasonably complete. It explains what the tool does and what information it returns, but it could improve by mentioning output format or error cases. However, it adequately covers the context for a straightforward getter tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with team_id well-documented in the schema itself. The description does not add parameter-specific details beyond what the schema provides, but since there is only one parameter and schema coverage is high, the baseline is 3. The description compensates by clarifying the output semantics (what information is returned), justifying a score of 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get detailed information') and resource ('about a specific support team'), distinguishing it from siblings like pylon_get_teams (which lists teams) and pylon_get_issues (which deals with issues). It specifies the scope of information returned (team members, roles, workload, performance metrics), making the purpose explicit and distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving details of a specific team, and the input schema's description for team_id provides guidance to 'Get this from pylon_get_teams first,' indicating a prerequisite. However, it does not explicitly state when not to use this tool or name alternatives beyond the implied sibling pylon_get_teams, so it lacks full exclusion criteria.
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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