TutorPing

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 most of the burden and does well: 1-business-day email reply, an external party (TutorPing) receiving data, and a hard consent precondition. The one real gap is that it doesn't say the call fails without consent, though the schema's const:true hints at it.

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 that are front-loaded with the action, then the trigger, then the turnaround, then the mandatory pre-call steps and privacy rule. No filler; each sentence adds a distinct operating constraint.

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 an 11-parameter, no-output-schema, write-to-external-service tool, the description supplies the process, the timing expectation, the consent gate, and the privacy constraint. An agent has everything needed to invoke it 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?

Schema coverage is 82% (baseline 3), and the description adds genuine meaning: consent is framed as the user's explicit agreement to share the request and contact details, and studentFirstName is constrained to a first name only. It doesn't clarify level/notes/schedule fields beyond the schema, so it sits slightly above baseline rather than at 5.

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?

Specific verb+resource ('Submit a request for TutorPing to match a student with a tutor') with the outcome named. It is immediately distinguishable from siblings search_tutors (search) and request_booking (booking a specific tutor).

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 the exact trigger condition: when search_tutors finds no fit or the user prefers to be matched, routing the agent from the sibling search tool to this one. It also specifies the pre-call workflow — collect needs, confirm details, obtain explicit consent.

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 the safety profile is known. The description adds useful behavioral context by enumerating what is returned (rate, reviews, page link, bookability) and the fallback routing rule, though it says nothing about result limits or pagination.

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 front-loaded sentences: purpose, return payload, then usage/routing. Each sentence carries distinct information with no 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?

With no output schema, the description usefully enumerates the return fields an agent needs. Annotations cover the read-only nature, and parameters are fully documented in the schema. Only pagination/result-count behavior is left unspecified.

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 all four parameters are already documented including examples and the online-tutor omission rule. The description only restates subject, level, and location and ignores maxHourlyRate, adding nothing 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?

States a specific verb ('Find') and resource ('independent tutors on TutorPing') scoped by subject, level, and location. It is clearly distinguishable from the singular get_tutor and the proactive request_tutor sibling.

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 audience context ('students or parents looking for a tutor') and names a fallback alternative ('If no listed tutor fits, use request_tutor'). It does not, however, clarify when to prefer get_tutor or get_tutor_availability over searching.

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 only readOnlyHint=true declared, the description carries most of the burden and does add real behavioral context: the data comes from Reddit and other monitored communities, it reflects recent activity, it is aggregated per community, and the underlying requests are restricted to TutorPing tutors. It does not state how far back "recently" reaches or how counts are computed, but the access-policy and data-source disclosures are valuable beyond the annotation.

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, front-loaded with the core measurement and followed by use case and access policy, with no filler. Slightly more compact phrasing could merge the second and third sentences, but each sentence contributes distinct 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?

For a read-only, two-parameter tool with no output schema, the description covers what is measured, the source, the aggregation level (by community), and the access restriction. The main remaining gap is the unspecified time window, which the agent must infer from the days parameter and its default.

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 50%: the subject parameter is documented in the schema ("SAT", "organic chemistry", "piano"), but days has no schema description. The description's "recently" loosely gestures at a time window but never mentions the days parameter, its default of 7, or its 14-day cap, so it does not compensate for the coverage 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 states a specific measurement (how many people recently asked for a tutor) applied to a specific scope (a subject), with an explicit data provenance (Reddit and other communities TutorPing monitors). It is clearly distinguishable from the sibling tools (get_tutor, search_tutors, request_booking), which all deal with tutors/bookings rather than aggregate demand, though it never names a sibling to contrast against.

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" gives an intended audience and a decision context, which is more than a bare purpose statement. However, there is no explicit when-to-use/when-not guidance and no alternative tool is suggested for related needs, so the usage framing stays implicit.

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