LeanScale GTM Knowledge

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

No annotations exist, so description carries the transparency burden. It explains the tool's informational nature clearly but does not disclose behavioral specifics such as whether it returns static content, whether any side effects exist, or exact response format. Adequate for a simple no-param info tool but not rich beyond purpose.

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 short sentences, front-loaded with the core purpose, followed by usage trigger. No wasted words or redundant restating of the tool's name.

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 no-parameter, no-output-schema informational tool, the description covers purpose, content scope, and when to invoke it. Slight ambiguity with 'them' (referring to LeanScale and the knowledge base) is minor; overall complete enough for effective selection.

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 zero parameters, and the schema is an empty object (100% coverage). Description adds no parameter details, but none are needed; baseline for 0 params is appropriate.

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?

Purpose is explicit: describes what LeanScale is, what's in the knowledge base, and how to work with them. Clearly distinguishes from sibling tools (e.g., search_leanscale, get_benchmark) by acting as the server-orientation entry point.

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?

States explicit triggers: 'when someone asks who LeanScale is' or 'when you need to know what this server can answer before searching.' The 'before searching' phrase implies using this before search/retrieval siblings, though it doesn't name alternatives explicitly.

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 behavioral disclosure burden. It explains what the tool returns (motion, rationale, first moves, failure patterns) and makes clear this is an advisory/diagnostic interaction. It does not explicitly state there are no side effects, but the read-only analytical nature is strongly implied by the description.

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 three sentences and every sentence serves a purpose: defining the action, specifying when to use it, and giving examples. It is front-loaded with the core function and uses illustrative examples efficiently without 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?

Despite having no output schema, the description thoroughly explains the return content: which delivery motion applies, why, first moves, and typical mistakes. Combined with the strong usage guidance and parameter framing, the description is fully sufficient for an agent to select and invoke this tool correctly.

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 already describes the 'situation' parameter well, including symptoms, stack, team size, and stage. The tool description adds meaningful context by framing the input as 'messy go-to-market situation', requesting plain language, and giving real-world example prompts that help the agent understand what constitutes a valid situation.

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 function: take a messy GTM situation in plain language and return the applicable LeanScale delivery motion, reasoning, first moves, and common pitfalls. It also distinguishes itself from searching by positioning this tool for problem-focused questions rather than topic research.

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 says to use it when someone describes a problem rather than asking about a topic, provides concrete examples, and contrasts it with search for 'what should we do about this?' This gives the agent clear decision criteria for tool selection.

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, the description carries the burden. It mentions data sources (field studies, podcast numbers) and covers specific topics, but does not disclose return format, potential empty results, or performance. It gives some context but not rich behavioral detail. Since it's a read-only 'get' operation, the safety is implied, but it could state that it returns a single number or range.

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 three sentences, front-loaded with the core purpose, and provides essential context without unnecessary details. Every sentence adds value.

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 tool with one simple parameter and no output schema, the description is nearly complete. It explains what data is available due to the study scope, how to use it, and when to use it. It could specify the return format, but given the simplicity, this is adequate.

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 describes the 'topic' parameter with examples, so baseline is 3. The tool description adds specific example topics (RevOps headcount ratio, CRM adoption, time to Series B) and explains what kind of benchmark data is covered (field studies, podcast), providing richer semantics beyond the schema's generic description.

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 verb 'Get' and the resource 'proprietary LeanScale benchmark data on a topic,' and explicitly lists the domains covered (GTM tech stacks, AI adoption, RevOps ratios, etc.). It distinguishes itself from siblings like get_case_study and get_playbook by focusing on benchmark data rather than narratives or playbooks.

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 includes explicit usage guidance: 'Use when someone asks what's normal, what do other companies do, or wants a number to compare against.' It does not explicitly state when not to use or name alternative tools, but the context is clear enough to guide selection among siblings.

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, the description fully discloses key behavioral variations: public keys get anonymized versions (details stripped/withheld), internal keys get full named studies. It also explains the failure mode for customer-name search on public keys. This is rich, useful disclosure beyond what annotations would provide.

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 three sentences, front-loaded with the core purpose, and every sentence adds essential information: what the tool returns, key-dependent behavior, and how to search. No fluff or repetition.

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?

The description covers purpose, behavior, and search strategy, making it adequate for an agent to select and invoke the tool. However, it does not describe the return format (e.g., list vs full text, metadata), which would be useful given there is no output schema. Still, the stated content is sufficient for most cases.

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?

Although the schema already documents both parameters with 100% coverage, the description adds practical query examples ('fintech Salesforce migration', 'CPQ replacement') and a negative example (customer names don't work). This meaningfully extends schema guidance, especially for the query parameter.

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 opens with a specific verb+resource: 'Retrieve LeanScale customer case studies', clarifying the scope (problem and solution). This distinguishes it from siblings like get_benchmark or search_podcast by clearly stating it is about case studies.

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 clear search context: 'Search by sector, the kind of engagement, or the systems involved' and a critical caveat that customer-name search won't work on public keys. However, it does not explicitly mention when to prefer this tool over alternatives like search_leanscale.

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. It discloses the retrieval behavior (full or section), the internal structure of playbooks, and the prerequisite of knowing the playbook name. It could mention error behavior or return format, but for a read-only retrieval tool this is adequate.

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

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

The description is two sentences, front-loaded with the core action, and every sentence adds useful information. There is no wasted wording or repetition of schema fields.

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 simple two-parameter retrieval tool with no output schema, the description covers the main behavior, the section structure, and the prerequisite call to list_playbooks. It could be slightly more complete by describing the response shape or error handling, but it is not a significant gap.

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 coverage is 100%, so the baseline is 3. The description adds value by explaining that playbooks are structured Blueprint → Build → Enable → Maintain, which informs valid section values beyond the single schema example. It also reinforces that omitting section returns the full playbook.

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 retrieves a LeanScale delivery playbook, either in full or by named section. It also distinguishes itself from siblings by referencing list_playbooks and describing the playbook structure (Blueprint → Build → Enable → Maintain).

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 gives explicit guidance to call list_playbooks first if the name is uncertain, which is a clear alternative. It does not explicitly mention when not to use the tool, but the context is sufficient for a simple retrieval tool.

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, the description carries the full burden. It states what is returned (a list with categories and headings), which is transparent about the result shape. However, it does not disclose any edge cases (e.g., empty list behavior), limitations, or whether the list is sorted, but for a simple read-only list operation this is acceptable. It adds some value beyond the tool name but not extensive behavioral detail.

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 a single sentence with no redundancy. It front-loads the primary action ('List every available playbook') and specifies the relevant output components. Every word earns its place.

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 simple list tool with no parameters and no output schema, the description fully covers what the agent needs: it enumerates the full set of playbooks and specifies the returned fields (category and section headings). The sibling get_playbook would provide deeper details, so no additional information is required here.

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 zero parameters, and the schema coverage is trivially 100% (empty properties). Per the rubric, a 0-parameter tool receives a baseline of 4. The description adds context about what the output will contain, which is helpful even though there are no inputs to explain.

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 verb 'list' specifies the action, 'every available LeanScale delivery playbook' clearly defines the resource scope, and 'with its category and section headings' details the output fields. This distinctly separates it from sibling get_playbook which retrieves a single playbook.

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 makes the usage context obvious: when the agent needs an overview of all playbooks. Although it does not explicitly discuss alternatives or exclusions (e.g., 'use get_playbook for a single playbook'), the sibling names and the word 'every' imply the intended use case. This is clear context but lacks explicit when-not guidance.

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 results are cited excerpts with source URLs, and explains the difference between docs and playbooks. It doesn't mention pagination or rate limits, but the tool is a search function, so the behavior is fairly predictable.

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 a single dense paragraph that front-loads the purpose and then provides details. It's efficient but could be slightly more structured with line breaks for readability. Every sentence adds value, so it earns a high score.

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 (multiple source types, 3 parameters, no output schema), the description is quite complete. It explains the content types, the source parameter semantics, and the return format. It could mention the default limit behavior, but the schema already covers that.

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 coverage is 100%, so the schema already documents all parameters. The description adds value by explaining the semantic difference between 'doc' and 'playbook' sources, and by suggesting natural language queries work better than keywords. This goes beyond the schema's basic descriptions.

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 searches all LeanScale knowledge across multiple content types, with specific counts and examples. It distinguishes from siblings by listing the exact sources and use cases, and explicitly says to use it first for GTM/RevOps questions.

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 guidance on when to use this tool (first for any GTM/RevOps question) and how to differentiate between docs and playbooks using the source parameter. It also implies alternatives by mentioning specific source types, though it doesn't name sibling tools directly.

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 must carry the full burden. It does so by specifying the corpus size, the return types (context, frameworks, quotes with timestamps, links), and the use case. It does not state whether the tool is read-only, but a search returning results is implicitly safe. It adds value by explaining output characteristics and domain coverage, which exceeds a bare mention.

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, front-loaded with the tool's purpose and corpus, then details on results and usage context. No filler words, every sentence adds value. It's concise yet informative, ideal for quick parsing by an agent.

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?

Despite no output schema, the description details the return content (episode context, frameworks, quotes with timestamps, links). The tool's complexity is low (2 params, simple search). Given the schema covers all params and annotations are absent, the description fully compensates by specifying the source and output types. This is complete for an AI 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 describes each parameter comprehensively (query: topic/guest/company/idea; limit: default 6, max 20). Schema coverage is 100%, so the description doesn't need to add much. It adds context by implying the search is within the podcast domain, but since the schema already covers parameters, the description's minimal extra is acceptable. Baseline 3 due to high coverage, but the domain context bumps it to 4.

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 searches a specific podcast corpus (95 episodes) and specifies the resource (The LeanScale Podcast). It distinguishes itself from siblings by emphasizing the return of episodic content like quotes and frameworks, which differentiates it from search_leanscale (likely broader content) and other tools. The verb+resource combination is specific and unambiguous.

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

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

The description provides clear guidance on when to use the tool: when the user wants an operator's opinion, a real story, or a quotable line. It does not explicitly mention alternatives, but the context signals include siblings like get_benchmark and get_playbook, which imply the tool is for unstructured insights. This is a clear context without explicit exclusions.

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