TutorPing

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

readOnlyHint=true already establishes a safe read, and the description adds real context beyond it: the response content (a recommended tool/page plus contact details) and a privacy constraint ('Do not include personal information'). It does not disclose rate limits, what happens to out-of-scope questions, or whether contact details are always present.

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 short sentences, all earning their place: capability, return value, and the privacy constraint. The fallback scope is front-loaded ahead of the output description, so an agent gets the routing decision first.

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 read-only routing tool with no output schema, the description covers what it does, what it returns, and the key constraint on input content. Minor gaps remain around out-of-scope behavior and how the optional askedFor parameter affects results, but nothing essential for a correct call 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 coverage is 50%: askedFor is documented via its enum, but the required 'question' parameter carries only min/max length with no description. The phrase 'in plain words' does convey that question is free-form natural language, which adds some value, but the description never addresses the askedFor dimension or the 500-char limit.

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 verb (Ask) and resource (TutorPing / tutor / student questions) and explicitly scopes it against siblings with 'that the other tools don't cover.' It also names the output ('which TutorPing tool or page fits, plus contact details'), so an agent can distinguish this fallback-router from search_tutors or get_tutor without opening any schema.

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 phrase 'that the other tools don't cover' gives a clear when-to-use condition relative to the sibling set, and 'in plain words' tells the agent this is the natural-language entry point. It stops short of naming a specific alternative (e.g. 'use search_tutors for browsing'), so the routing is implied rather than fully explicit.

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?

readOnlyHint=true already covers the safety profile, so the description only needs to add context. It adds that the profile is 'public' and scoped to one tutor, but does not mention auth needs, rate limits, or what happens on an unknown slug. Since no output schema exists, the return-content description ('full public profile') is thin.

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?

A single tight sentence that front-loads the verb and resource and packs the slug provenance into a parenthetical. No wasted words.

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 one-parameter lookup with no output schema, the description establishes scope and the upstream source of the identifier. It is largely sufficient, though it could say a little more about what the returned profile contains given there is no output schema to fall back on.

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 0% and the single required 'slug' parameter has no inline description. The description partially compensates by telling the agent the slug originates from search_tutors, but it omits any format, casing, or length constraints (schema maxLength 80 is undocumented).

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 verb ('Get') and resource ('full public profile for one TutorPing tutor'), with scope narrowed to a single record. It also hints at the sibling relationship by noting the slug comes from search_tutors, though it doesn't fully contrast against get_tutor_availability or other siblings.

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 phrase 'by slug from search_tutors' implies this is a follow-up lookup after a search, but there is no explicit when-to-use or when-not guidance, nor any stated alternative for retrieving tutor data.

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?

readOnlyHint=true already tells the agent this is a safe read. The description usefully adds that returned times are in the tutor's timezone, which affects interpretation of results, but it omits pagination, how many slots come back, or anything about the lookahead default.

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 tight sentences, front-loaded with the resource and scope, with the sequencing hint second. 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?

With no output schema, no parameter documentation, and minimal annotations, the description should carry more. It does not explain what 'days' controls or what a returned slot looks like, which are exactly the gaps an agent would need filled for a 2-param, undocumented tool.

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 0%, so the schema supplies only types, a default (14), and a 1-30 range for 'days' with no stated meaning. The description never mentions either parameter, leaving 'slug' (presumably the tutor identifier) and the lookahead window for the agent to guess.

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 verb+resource ('Open session times for a tutor') and adds the timezone scope, so the agent knows what it retrieves. It does not explicitly contrast itself with siblings like get_tutor or search_tutors, but the purpose is unambiguous.

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

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

'Use before request_booking' gives clear sequencing context for when this tool belongs in a flow. It stops short of stating when-not to use it or naming alternative availability sources.

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 readOnlyHint annotation already signals a safe, non-mutating operation. The description adds useful context that the content covers lead generation and connecting an AI assistant, but it does not describe the return format or any other behavioral traits.

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, front-loaded sentence with no filler. Every word contributes to stating what the tool explains.

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, read-only informational tool with no parameters and no output schema, the description covers the essential subject matter. It could be slightly more explicit about instructing the agent to use it for user questions on this topic, but it 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 tool takes zero parameters, so there is no parameter semantics burden. The baseline for zero-parameter tools is 4, and the description appropriately avoids inventing parameters.

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 gives a specific verb ('Explains') and a specific resource ('how a tutor gets student leads from TutorPing'), so the tool's purpose is clear. It does not explicitly distinguish itself from the sibling tools, which are all data/action endpoints, but the informational nature makes it distinguishable in practice.

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?

There is no guidance on when to use this tool versus alternatives. Usage is only implied by the tool name and description; an agent must infer that this is appropriate for onboarding/FAQ-style questions about lead generation.

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 only declare openWorldHint and destructiveHint=false; the description adds real behavioral value beyond them by disclosing side effects (confirmation emails to both tutor and student) and the mandatory human-confirmation step before calling. It still does not say what happens on a failed or conflicting booking.

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, zero filler, and the action plus data source are front-loaded before the operational constraint. Every sentence carries a distinct instruction.

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 mutation tool with no output schema, the description covers purpose, prerequisite source call, user-confirmation workflow, and side effects. It omits the result of a successful call and error handling for unavailable slots, which an agent would need for a booking flow.

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 only 43% across 7 parameters, so the description needs to compensate but largely does not. It references time/name/email only as a confirmation checkpoint, leaving slug, subject, and type semantics entirely to the schema — and slug and subject have no schema descriptions at all.

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 verb and resource ('Book a student into one of a tutor's open slots') and scopes it against the sibling that produces the slot data (get_tutor_availability). An agent can distinguish this from get_tutor_availability or search_tutors without opening a schema.

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 ties invocation to output from get_tutor_availability and adds a required pre-call workflow: confirm time, name, and email with the user. It does not state any when-not conditions or failure-path alternatives (e.g., what to do if the slot is taken).

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 only declare openWorldHint/destructiveHint, so the description carries the real behavioral load and does: it discloses the response channel and SLA (email within 1 business day with tutor options), the consent precondition, and a privacy constraint (first name only, never a child's full name). These are non-obvious operational and compliance traits an agent must enforce before calling.

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?

Four sentences, front-loaded with the action and routing rule, then prerequisites, then the privacy guardrail. Dense and mostly waste-free, though the confirm/consent sentence is slightly clause-heavy.

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 12-parameter, 3-required open-world submission tool with no output schema, the description covers what is needed: trigger, prerequisites, consent gate, privacy limit, and what the caller gets back (email with tutor options within 1 business day). Nothing an agent needs to call it 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?

Schema description coverage is 83%, so most parameters are already documented and the baseline is 3. The description adds genuine semantic weight: it ties 'collect what the student needs' to the input set and constrains studentFirstName to a first name only, while consent is framed as requiring the user's explicit agreement rather than a flag to set silently.

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 verb and resource (submit a request for TutorPing to match a student with a tutor) and immediately scopes it against the sibling it replaces. An agent can distinguish this from search_tutors and request_booking without opening any schema.

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 triggering condition: use when search_tutors finds no fit or the user prefers to be matched. It also lists the pre-call sequence (collect needs, confirm details, obtain explicit consent) and the alternative, leaving nothing to inference.

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=true and openWorldHint=false, so safety is covered; the description adds return-shape detail (public page tutors vs. anonymous matched profiles vs. AI tutors) plus a genuine behavioral obligation not captured anywhere else: 'Always tell the user that an AI tutor is AI.' That policy instruction is real added value, though permission/rate-limit behavior is untouched.

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?

Purpose is front-loaded in the first clause, which is good, but the middle sentence is a dense run-on stacking three different result categories and their sub-fields inside parentheses. The information is useful but would be far easier to parse as separate sentences or a short list.

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?

There is no output schema, so the description correctly carries the burden of explaining what comes back (public tutors, anonymous matched profiles, AI tutors) and it also flags the AI-disclosure obligation. It is close to complete for a read-only search tool, with only pagination/result-count mechanics left unstated.

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 filters with examples, and the baseline is 3. The description echoes the filter dimensions (subject, level, location) and hints at rate via the returned 'rate' field but adds no syntax or semantics beyond what the schema provides.

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?

Names a specific verb and resource (find independent tutors on TutorPing) and scopes it by subject, student level, and location, which distinguishes it from siblings like get_tutor and get_tutor_availability. The audience and subject domains are spelled out, so an agent can recognize the intent without opening the schema.

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 explicitly when to use it (students or parents looking for a tutor, listing SAT/ACT, math, science, languages, music) and routes to the alternative when the condition fails: 'If no listed tutor fits, use request_tutor and TutorPing will match one.' That is a concrete when-to-use and when-to-switch rule.

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 it read-only, so the description adds real value: it discloses data provenance (Reddit and other monitored communities), the aggregate-only scope ("in total"), and an access restriction — the underlying requests are shared only with TutorPing tutors. It doesn't cover freshness/latency or update cadence, but the disclosure is well above the annotation baseline.

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 tight sentences that lead with the core metric definition, then add usage context and an access caveat. Near-zero waste, though "in total" is slightly redundant with the opening "How many people."

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 no output schema, the description correctly carries the return-value burden by framing the result as a total count. Combined with the data-source and access-restriction notes, an agent has enough to call it correctly for a simple two-parameter tool.

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 50%: subject is documented with examples, while days relies on its default/min/max constraints. The description reinforces the subject concept and the "recently" window but gives no detail on the days parameter's meaning, so it only marginally compensates for the 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?

States a specific resource and metric: a count of recent tutor requests for a given subject, sourced from Reddit and monitored communities. An agent can distinguish this aggregate-demand tool from siblings like search_tutors or get_tutor, though no sibling is named explicitly.

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?

"Useful for tutors deciding whether there is demand for their subject" implies the use case but never states when to use this versus alternatives (search_tutors, ask_tutorping, how_to_get_leads) or any prerequisite. Guidance is present but left to inference.

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