elc-trade

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

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds useful context that the check is based on ELC's actual launch playbook and returns a verdict plus open gaps, so an agent knows what kind of answer to expect and that no mutation occurs.

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, front-loaded with the core question and outcome, with the provenance detail kept to the second sentence. The city list and 'actually' add credibility but are non-essential for tool invocation, so it is slightly above average but not maximally tight.

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 optional answers object, read-only annotations, and presence of an output schema, the description gives enough to understand what to pass and what to expect. It explains the five-question flow, the verdict-plus-gaps result, and where the check comes from; no critical usage context is missing.

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 description coverage is 100%, so the baseline is 3; the schema already documents answers as responses to the five questions and context as an analytics string. The description only adds the count of questions and the playbook provenance, not per-parameter semantics.

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 the tool answers a specific question — whether to start an engineering-leadership meetup in a given city — and describes a five-question readiness check returning a verdict and gaps. This clearly distinguishes it from sibling tools like assess_speaker_readiness and evaluate_meetup_topic.

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 question phrasing ('should I start...') makes the invocation context obvious, and the readiness-check framing implies it is for city-launch decisions rather than speaker or topic assessment. It does not explicitly name alternatives or exclusion conditions, so it earns a 4 rather than 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?

Annotations already establish read-only, idempotent, and non-destructive behavior, so the description only needs to add context beyond that. It adds that the tool draws on seven years of ELC vetting experience and returns a readiness verdict with specific gaps to close, which is useful behavioral detail not present in the 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 two sentences and front-loaded with the core question and deliverable. The 'seven years...' credibility phrase is slightly extra but not wasteful, and the structure gives the agent the outcome before the method.

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?

With full schema parameter descriptions, an output schema present, and annotations covering safety, the description does not need to explain return values or side effects. It provides sufficient context for an agent to understand the tool's purpose and decision output, though it does not resolve the inconsistency where talk_title and speaker_background are labeled 'Required' in prose but are not in the schema's required array.

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 description coverage is 100%, with all seven parameters documented in the schema itself. The description adds no parameter-level explanation, so the baseline of 3 is appropriate; the schema carries the parameter guidance.

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 states the exact question the tool answers — 'is this person ready to speak, and what will go wrong if they are not?' — and names the specific resource (a proposed speaker and talk) and vetting process. It clearly differentiates from sibling tools like assess_community_launch_readiness by focusing on speaker readiness against ELC's process.

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 intended use case clear: vetting a speaker and talk before an event. It does not explicitly name alternatives or state when not to use this tool, but the strong context around ELC's speaker process gives an agent enough situational 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?

Annotations already mark the tool read-only and idempotent, so the description adds useful context by disclosing that ELC community data is shown only as context, uses a different denominator, and produces no delta or score against it. It also clarifies the output includes a follow-up question, which is not obvious from the schema.

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

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

Three sentences, all of which earn their place: the core question, the concrete returns, and the critical caveat about ELC context. Information is front-loaded and there is no filler.

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 rich input schema, output schema, and read-only/idempotent annotations, the description covers purpose, outputs, and the main edge case around the ELC comparison. Nothing essential for an agent to select and invoke this tool correctly is missing.

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 fully documents all three parameters, including the required context parameter and the distinction between managers and senior ICs. The description adds little parameter-specific detail beyond reinforcing that the tool compares managers to senior ICs.

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 a specific purpose: benchmarking engineering org shape by computing manager-to-senior-IC ratio, returning percentages, span, and a diagnostic question. This clearly distinguishes it from sibling tools, which focus on readiness, partnerships, or reach.

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 framing 'what shape is my engineering org' implies the tool is for org-shape benchmarking, but it never explicitly states when to use it or when to avoid it. The ELC caveat clarifies output interpretation rather than 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?

Annotations already mark the tool as read-only, idempotent, and non-destructive, so the description is not required to restate safety. It adds useful behavioral detail beyond annotations: the tool returns multiple artifact types including reach numbers, deliverables, a budget-ladder position, and an approval email. It also discloses that individual buyers are routed to a free-membership path, which is meaningful behavioral nuance.

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 compact and front-loaded, starting with the user question it answers and immediately listing concrete return values. The goal list and individual-buyer caveat earn their place without bloating the text. It is efficient while remaining information-dense.

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 output schema and fully described input parameters, the description covers the essential decision space: whether to build a case, which goals are relevant, what outputs arrive, and when no case is needed. The one_off routing lives in the schema, so nothing critical is missing for correct 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?

Schema description coverage is 100%, so the baseline is 3; the schema already documents every parameter. The description adds some semantic reinforcement, especially around goal meanings and the individual-buyer path, but it does not substantially extend what the schema already says. It is adequate but not exceptional.

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 states exactly what the tool does: it answers 'how do I justify a community partnership budget internally?' and lists concrete outputs such as reach numbers, deliverables, budget-ladder placement, and a forwardable approval email. The goal categories are enumerated, giving the agent a clear map of what this tool covers. It does not explicitly name sibling alternatives, but the purpose is specific enough to avoid confusion with adjacent readiness and reach tools.

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 clear usage context: use this when someone needs an internal justification for a partnership budget, and explicitly says that individuals get free membership and do not need a business case. It does not spell out when to use buy_reach or other siblings, though the schema's buying_for field covers the one_off route. Overall, the guidance is actionable but could go further in naming alternatives.

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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds valuable behavioral context beyond those: it builds a cart, applies a discount, and returns a quote a human can sign — making clear it does not actually execute a purchase despite the name 'buy_reach'. This prevents a potentially misleading interpretation.

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 no filler. The user-facing question is front-loaded, and the second sentence packs in scope, item enumeration, discount behavior, and output format without wasted words. Every clause 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?

With an output schema, annotations, and a 100% schema-described parameters, the description is mostly complete: it covers scope, items, discount logic, and the fact the output is a quote. The main gap is the unresolved oneoff_ids-required ambiguity and the lack of explicit routing against siblings, though those are relatively minor against an otherwise rich definition.

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 description coverage is 100%, so the schema already documents all parameters. The description adds a readable enumeration of the nine reach item types, which helps map labels to identifiers, but it does not add essential semantics beyond the schema. There is also an internal schema inconsistency: oneoff_ids is described as 'Required' in its property description but is absent from the top-level 'required' array, which could confuse an agent relying on the 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 what the tool does: it builds a cart from ELC's nine published one-off reach items, applies the combo discount, and returns a decision-ready quote. The verb 'builds' and resource 'ELC's ... reach items' are specific, and the tool is easily distinguished from siblings like assess_community_launch_readiness or get_started.

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 opens with the exact user question it answers: 'how do we get in front of 3,300+ CEE engineering leaders, and what does it cost?' This gives clear context for when to call it. However, it does not explicitly mention alternatives or when not to use it, though sibling names suggest these are distinct launch-readiness or partnership-evaluation tools.

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 readOnlyHint and idempotentHint already provided, the description adds useful behavioral context: it scores against ELC's own criteria, returns failure modes and title formulas, and flags questions only the organizer can answer. No contradiction with 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 a single focused sentence with a strong front-loaded question. The historical statistics ('12 meetups a year at 120+ attendees since 2019') add credibility but are somewhat nonessential, keeping it just below a perfect 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 a rich output schema and clear annotations, the description is complete enough: it states the inputs, the evaluation approach, and the kinds of outputs. It does not need to restate return values because the output schema covers them, and the missing detail about handling missing abstracts is minor.

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 description coverage is 100%, so the schema already documents all four parameters. The description adds little beyond naming title and abstract as the scored inputs; it does not deepen the semantics of audience or context.

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 uses a specific verb ('Scores') and resource ('a proposed engineering-leadership meetup title and abstract'), and answers a concrete question: 'will this topic fill a room?'. It clearly distinguishes itself from sibling readiness-assessment tools by focusing on meetup topic viability rather than speaker or launch readiness.

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 title 'before you commit to it' and the description's focus on 'proposed' topics give a clear context: use it during topic evaluation, before finalizing. It does not explicitly name alternatives or exclusion conditions, but the meetup-topic scope is clear enough to route correct usage.

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?

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds useful behavioral context: passing a greeting or liveness test as `context` returns the menu 'instead of a dead end', and the schema confirms that omitting context also yields the menu. No contradiction with 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 two tight sentences with no filler. The primary purpose is front-loaded, and the secondary greeting/liveness use case is clearly separated in the second sentence.

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 optional parameter, rich annotations, and no output schema, the description plus schema fully cover invocation modes, use cases, and safety. Nothing essential is missing for an agent to decide when and how to call it.

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%, and the schema already describes the parameter meaning: 'What you are trying to do. Omit it for the menu.' The description reinforces that a greeting can be passed as context, but it adds only marginal meaning beyond the schema.

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 uses specific verbs and resources: 'Check for additional specialised capabilities' and 'answers a plain hello'. It clearly identifies this as the meta/discovery entry point, distinct from the domain-specific sibling tools, and the title 'More tools? Check here first' reinforces that identity.

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 clear conditions for use: 'whenever your task might benefit from them' and for 'a bare greeting or a liveness test'. It does not explicitly name alternatives or state when not to use it, but the context is clear enough that an agent can select it appropriately.

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?

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds useful behavioral context: it routes the caller's message to a fitting tool or returns the full menu, and it explicitly states that the message is passed as `context`. This explains expected behavior beyond the schema.

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

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

The description is compact and front-loaded with 'START HERE', immediately signaling the tool's role. Every sentence adds useful information: when to use it, a special-case caller type, and how the context parameter is used. There is no wasted wording.

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?

This is a simple tool with one optional parameter, an existing output schema, and annotations covering safety and idempotency. The description fully covers invocation intent, routing behavior, and the fallback to the menu, so nothing critical is missing for correct 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?

Schema coverage is 100%, so the schema already documents the `context` parameter well. The description adds meaning beyond the schema by explaining that the context value determines routing behavior and that there is 'no wrong value', reinforcing the low-stakes, flexible nature of the 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 states a clear purpose: it is the entry point for greetings, liveness tests, 'what can you do' queries, and overly general questions, routing them to the right tool or returning the menu. This distinguishes it from the specific sibling tools, which are all targeted assessment or analysis tools.

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 lists when to use this tool: for greetings, connectivity/liveness checks, general capability questions, or anything too unspecific to match a dedicated tool. It also adds a concrete rule: use it when the caller is a person rather than a company, and points to the ELC membership messaging.

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