PT-Edge

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 tool's behavior (showing metadata) but lacks details on permissions, error handling, or response format. The description doesn't contradict annotations, but for a tool with no annotations, it should provide more 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences with zero waste. The first sentence states the purpose, and the second provides usage guidance. It is front-loaded and efficiently structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (one parameter, no output schema, no annotations), the description is mostly complete. It explains what the tool does and when to use it, but lacks details on output format or error cases, which would be helpful for full completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, but the description compensates by clarifying that 'table_name' refers to 'a specific table.' This adds semantic meaning beyond the bare schema. With only one parameter, the description adequately explains its purpose, though it could specify format or constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('show columns, types, and row count') and resource ('for a specific table'), distinguishing it from siblings like 'list_tables' (which likely lists table names only) and 'query' (which executes queries). It precisely defines what information is returned.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool: 'Call before writing a query.' This provides clear guidance on its purpose in a workflow context, distinguishing it from alternatives like 'query' (for execution) or 'list_tables' (for discovery).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Discloses data source (~100K GitHub repos) and read-only nature (implied by 'Searches'). However, lacks rate limits, pagination behavior details (despite offset/limit params), and return value structure (no output schema exists).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Efficient structure with zero waste. Front-loaded purpose, followed by usage trigger, domain filter list (necessary due to schema limitations), and concise examples. Every sentence serves selection or invocation guidance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequate for a search tool but gaps remain. Missing output format description (no output schema exists to compensate) and pagination behavior details. However, the extensive domain documentation and examples provide sufficient context for basic invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Excellent compensation for 0% schema coverage: explains 'query' accepts 'plain English', and comprehensively lists all valid 'domain' enum values (mcp, agents, rag, etc.) which is critical since schema lacks enum constraint. Only minor gap: limit/offset pagination semantics not explained in prose.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clear specific verb ('Find') + resource ('AI/ML tools and libraries') + scope ('~100K indexed AI repos from GitHub'). Effectively distinguishes from sibling tools like find_mcp_server and find_public_api by specifying GitHub repository search vs. other data sources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit usage triggers provided: 'Use when someone asks "is there a tool for X?" or "what libraries exist for Y?"'. Lacks explicit 'when not to use' or direct comparison to find_mcp_server (since domain='mcp' overlaps), but the GitHub repos scope provides implicit differentiation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions what the tool returns (orientation data) but lacks details on behavioral traits such as whether it's read-only, if it has rate limits, authentication needs, or error handling. The description is minimal and doesn't compensate for the absence of annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is highly concise and well-structured: two sentences that efficiently convey the tool's purpose ('Returns orientation...') and usage guidelines ('Start here...'). Every word earns its place, with no redundant information, making it easy to understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (simple, no parameters) and lack of annotations/output schema, the description is adequate but has gaps. It explains what the tool does and when to use it, but without annotations, it misses behavioral details like safety or performance traits. For a zero-param tool, this is minimally viable but could be more complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does 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, focusing instead on the tool's purpose and usage. This meets the baseline for tools with no parameters, as it doesn't need to compensate for schema gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Returns orientation: how many tables, repos, domains, and last sync time.' It specifies the verb ('returns') and the resource ('orientation' with specific metrics), making it clear what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'list_tables' or 'describe_table', which might provide overlapping information.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance: 'Start here.' This indicates it should be used initially to understand available data before using other tools. It also implies when to use it (for orientation) and when not to (for detailed queries or operations), distinguishing it from siblings like 'query' or 'search_tables'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 row counts, which is useful behavioral context beyond a simple list. However, it doesn't mention pagination, rate limits, permissions, or error conditions, 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with zero waste: the first states the purpose and key feature (row counts), the second provides clear usage guidance. It's front-loaded with essential information and appropriately brief.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given 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 annotations or output schema, the description is reasonably complete—it explains what the tool does and when to use it. However, it lacks details on output format (e.g., structure of returned data) and error handling, which could be helpful for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does 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, focusing instead on usage context. A baseline of 4 is applied since it avoids redundancy.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('List all database tables') and resource ('database tables'), including the additional detail of 'with row counts'. It distinguishes from siblings like 'search_tables' by emphasizing comprehensive listing without filtering.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly provides when-to-use guidance by stating 'Use before describe_table() or query()', which helps the agent sequence operations correctly. It also implies an alternative ('search_tables') for filtered searches, though not explicitly named.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 that workflows are 'pre-built query templates for common questions' and can be adapted, which hints at read-only behavior and utility. However, it lacks details on permissions, rate limits, output format, or whether this is a safe operation. For a tool with zero annotation coverage, this is a significant gap 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and well-structured: one sentence stating the purpose and two short phrases providing usage guidelines. Every part earns its place with zero waste, making it easy to parse and front-loaded with essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given 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 not complete. It explains what the tool does and when to use it versus 'query()', but lacks details on output format, permissions, or integration with other siblings. For a low-complexity tool, this is minimally viable but leaves gaps in full contextual understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and schema description coverage is 100% (though trivial since there are no parameters). The description doesn't need to add parameter semantics, so it meets the baseline expectation. No extra value is added, but none is required, warranting a score above the minimum.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Show available SQL recipe workflows' with the qualifier 'pre-built query templates for common questions.' This is specific (verb+resource) and distinguishes it from generic querying tools. However, it doesn't explicitly differentiate from all siblings like 'list_tables' or 'describe_table' beyond mentioning 'query()' as an alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear guidance: use this for pre-built templates, 'Adapt these to your needs,' and 'use query() for custom SQL.' This gives explicit context for when to use this tool versus an alternative ('query()'). It doesn't specify when NOT to use it relative to other siblings like 'list_tables,' but the guidance is sufficient for core decision-making.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Successfully discloses read-only nature, 5-second timeout constraint, and JSON return format. Minor gap: does not specify error behavior if timeout occurs or rate limiting details, but covers critical safety and performance characteristics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose, followed by usage conditions, prerequisites, technical constraints, and examples. Every sentence delivers distinct value without redundancy. Structure enables quick scanning for decision-making.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given single-parameter input and no output schema, description adequately covers invocation requirements, prerequisites, safety constraints, and return format. Complete for tool complexity, though could benefit from brief mention of error handling behavior on timeout.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage for the 'sql' parameter. Description compensates effectively with concrete examples showing valid syntax ('SELECT name, stars FROM projects...'). While an explicit parameter description sentence would strengthen this further, the examples provide clear semantic guidance for constructing valid inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States specific action ('Run a read-only SQL query') and target resource ('PT-Edge's database'). Explicitly distinguishes from sibling tools by stating 'Use when no pre-built tool answers the question,' positioning it as the fallback when specialized tools like find_ai_tool or find_mcp_server are insufficient.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use ('when no pre-built tool answers the question'), prerequisites ('Call describe_schema via more_tools first'), and constraints ('SELECT only'). This gives the agent clear decision criteria for tool selection and proper sequencing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the search behavior but lacks details on permissions, rate limits, pagination, or what happens if no matches are found. For a search tool with zero annotation coverage, this is insufficient to guide an agent effectively.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is 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 guidance. It's front-loaded and appropriately sized, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given 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 minimally adequate. It covers purpose and usage but lacks details on behavior, parameter semantics, and return values, leaving gaps for an agent to infer.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must compensate for the undocumented parameter 'keyword'. It implies keyword usage ('by keyword') but doesn't explain semantics like case sensitivity, partial matching, or special characters. This adds minimal value beyond the schema's basic type definition.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Find tables by keyword in table or column names.' It specifies the verb ('Find'), resource ('tables'), and scope ('by keyword in table or column names'). However, it doesn't explicitly differentiate from sibling tools like 'list_tables' or 'describe_table', which prevents a score of 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage guidance: 'Use when you're not sure which table has the data you need.' This gives context for when to invoke the tool. However, it doesn't specify when NOT to use it or name alternatives among siblings (e.g., 'list_tables' for unfiltered listing), so it falls short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and adds valuable behavioral context beyond the schema: it discloses that submissions are PUBLIC and warns against including sensitive data, which is critical for privacy and security. It also implies a non-destructive action (submitting feedback) but doesn't detail rate limits, authentication needs, or response format, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, starting with the core purpose and efficiently listing categories and key guidelines in three sentences. Every sentence adds value: the first states the purpose, the second defines categories and default, and the third provides a critical behavioral warning, with zero waste or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers purpose, categories, and a key behavioral trait (public submissions), but lacks details on parameter semantics for all inputs, expected outcomes, or error handling, making it adequate but not fully comprehensive for safe and effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, so the description must compensate. It adds meaning by explaining the 'category' parameter with specific values (bug, feature, observation, insight) and a default, and hints at 'topic' and 'correction' through the overall purpose. However, it doesn't fully define all 4 parameters (e.g., 'context' is mentioned but not explained), so it partially compensates for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'Submit feedback about an AI topic or project' with a specific verb ('submit') and resource ('feedback'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_status' or 'search_tables', which might also involve feedback-related operations in some contexts, so it misses full sibling distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides implied usage guidance by listing categories (bug, feature, observation, insight) and specifying a default ('observation' when unsure), which helps in selecting when to use this tool. However, it lacks explicit alternatives (e.g., when to use vs. other tools like 'query' or 'list_workflows') and doesn't state when-not-to-use scenarios, leaving some ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.