Pressure Atlas

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

Annotations already declare destructiveHint=true and idempotentHint=true, and the description adds non-obvious behavior beyond them: closing is irreversible, closed items stay on record, the user must be consulted first, and the action does not consume the daily quota — the last detail is unavailable anywhere else.

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

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

Front-loaded with the core action and the finality constraint, then the user-consultation caveat and quota note. All sentences carry information, though the quota sentence is a slight tail-end addendum rather than tightly integrated.

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 present, return values need no explanation, and the annotations cover safety semantics. What remains — irreversibility, record retention, user-consent requirement, quota immunity — is fully covered, so an agent has everything needed to call this 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?

There is only one parameter and schema description coverage is 100%, so the schema already documents that id is the feedback id as returned by send_feedback and list_feedback. The description adds no syntax, format, or sourcing detail beyond the schema, so the baseline of 3 applies.

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 (close) plus resource (feedback this account sent) and an explicit scope qualifier about when the user considers it resolved or unwanted. It also implicitly separates itself from edit_feedback by stating closing is final and feedback cannot be changed once closed.

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?

Gives the triggering condition ('when the user says it is resolved or no longer wanted'), a required precondition ('Ask the user first'), and names the alternative path for reopening ('raising it again means sending new feedback'), which routes the agent to send_feedback.

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 declare the safety profile (non-read-only, idempotent, non-destructive) already, so the description's job is to add context — and it does: message is a full replacement not a patch, prior versions are retained by the team, closed feedback is immutable, and the call is exempt from the daily quota. These are non-obvious behavioral traits that the annotations cannot convey.

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

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

Front-loaded with the core action and its contrast to send_feedback, then layered preconditions and quota note. Every sentence carries distinct information, though the paragraph is denser than strictly necessary for a 4-parameter tool.

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?

An output schema exists, so return values need not be described. With annotations covering the safety profile and the description covering replacement semantics, immutability of closed feedback, versioning, quota, and a user-consent instruction, nothing an agent needs to call this 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 coverage is 100%, so the baseline is 3, but the description genuinely adds semantics the schema does not: 'message replaces the whole text, so send the complete updated wording, not just the change' clarifies replacement vs merge behavior, and 'kind and about change only if given' clarifies optional-update semantics. This goes beyond restating field names.

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 ('Change feedback this account already sent') and immediately distinguishes itself from siblings by naming where the id comes from (send_feedback or list_feedback). An agent can tell this apart from send_feedback and list_feedback 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?

Explicitly frames the choice against the alternative ('when the user wants to add to, correct, or refine it, instead of sending it again'), names the prerequisite id sources, states the 'Ask the user first' precondition, and gives an exclusion ('Closed feedback cannot be changed'). When-to-use, when-not-to-use, and alternatives are all present.

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, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds what the payload contains, which is genuine behavioral context, but says nothing about auth, rate limits, or behavior on a missing/invalid id.

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 sentence that front-loads the action and resource, then the id source, then the return content. No padding or redundancy.

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

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

An output schema exists, so return values need not be detailed, yet the description briefly characterizes them anyway. For a one-parameter read tool with full schema coverage and annotations, this is essentially complete; only missing-id error behavior is absent.

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 there is only one parameter, so the schema carries the load. The description still adds value beyond it by giving a concrete id example ('dataset:macro') and clarifying the id originates from search results.

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 (fetch) and resource (document by id), scopes the source to 'search results', and names what is returned ('description plus recent daily pressure readings'). This is enough to distinguish it from the metric/pressure sibling tools, though it doesn't explicitly contrast with them.

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?

'From search results' implies the tool is used after a search to drill into a result, which is useful contextual guidance. However, there is no explicit when-to-use versus alternatives (e.g., get_latest_pressure, get_recent_pressure) and no stated prerequisites.

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 this as a safe read-only, closed-world operation, so the safety profile is covered. The description goes further by defining 'closed-day' data freshness, enumerating the five ordered state values, explaining flow semantics, and explicitly warning that readings are measurements rather than predictions — a genuinely useful guard against misuse.

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 return-value semantics are front-loaded in the first sentence, and the closing caveat earns its place by setting correct expectations. The parenthetical enumerations are dense but informative rather than redundant.

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?

An output schema exists, so return structure need not be restated, and the description instead supplies the interpretation an agent needs for the enumerated values plus an access-scope note. The one real omission is routing relative to get_recent_pressure.

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 single 'dataset' parameter is fully documented in the schema (100% coverage, default 'macro', pointer to list_datasets), so the schema does the heavy lifting. The description adds nothing about the dataset parameter, which is acceptable at this coverage level but earns only the baseline.

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 ('newest closed-day pressure reading') and enumerates exactly what fields come back (state, flow direction, acceleration). It does not, however, differentiate itself from the sibling get_recent_pressure, leaving an agent to guess how 'latest' differs from 'recent'.

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 when-to-use statement and no mention of alternatives such as get_recent_pressure or fetch. The only implicit guidance is 'your access includes,' which hints at permission scoping but does not tell the agent under what conditions this tool is the right choice.

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 cover the safety profile (readOnly true, destructive false, openWorld false), so the bar is lower; the description still adds real behavioral context — the value space for each row (state, flow, acceleration), the CSV-per-day granularity, and the access-scoped depth constraint. The closing caveat that readings are measurements, not predictions or recommendations, further clarifies semantics.

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

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

Front-loaded with purpose and output format, then access constraints, then the semantic caveat. The parenthetical enumeration of state values is dense but informative; the whole thing is three sentences with minimal waste.

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 present, return values needn't be explained, yet the description usefully characterizes the CSV layout and field domains. Combined with the access-depth pointer to list_datasets and the annotations covering safety, the definition is nearly complete; only the sibling routing 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 schema already documents dataset, date_start, and date_end with patterns, defaults, and inclusivity. The description only restates the window concept and access depth, adding no new syntax or format detail beyond the schema. Baseline 3 applies.

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 verb and resource ('Daily pressure for a window') plus the output shape ('compact CSV, one row per day'), so an agent knows exactly what it returns. It does not, however, name or distinguish itself from the obvious sibling get_latest_pressure, which differs only by scope.

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 implied usage — it serves the window of days your access includes — and it points to list_datasets for determining depth, which is a useful prerequisite hint. But it never states when to prefer this over get_latest_pressure or fetch, and gives no explicit exclusions.

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, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds non-obvious quota-accounting semantics (free against the daily quota, one per-minute slot per call) that the annotations do not convey, though it does not discuss error states or throttling behavior when the limit is already reached.

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, no filler: the first front-loads what the tool returns, the second front-loads the quota accounting caveat. Every clause carries 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?

With zero parameters, an existing output schema, and annotations covering the safety profile, the remaining burden is the quota-cost nuance — and the description supplies exactly that. Nothing an agent needs in order 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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a parameterless tool applies.

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 (the caller's quota position) and enumerates what it returns: calls used today, calls in the last minute, the limits, and reset timing. This is far more precise than the title and clearly separates it from siblings like fetch, search, or get_latest_pressure.

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?

Gives clear invocation context: it does not consume the daily quota, but each request does consume a per-minute slot — a real cost signal an agent needs before polling. It stops short of naming when-not-to-use or an alternative (none of the siblings overlap), so it is 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.

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered structurally. The description adds the useful detail that the result reports the caller's access scope, but says nothing about ordering guarantees, auth prerequisites, or whether the list can be empty – a modest addition over 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?

Two short sentences with zero filler. The return content comes first and the actionable instruction ('Call this first') is placed at the end where it lands as a directive.

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 present and zero parameters, the description does not need to document return shape, and it correctly stays brief. It is nearly complete for this tool, with only the lack of any indication of an empty/unauthorized result state keeping it from a 5.

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 no parameters, so there is nothing for the description to disambiguate; the baseline for a zero-parameter tool applies. No parameter meaning is needed or missing.

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 verb and resource ('List the available datasets') and even names what comes back ('slug and what your access includes'). It is clear on its own, but it never differentiates itself from the many sibling list/get/search tools (list_feedback, get_usage, search), so an agent must infer the distinction from the name alone.

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?

'Call this first' is explicit sequencing guidance that tells the agent when in the workflow to invoke it. It does not name an alternative or a condition under which a sibling is preferable, but for a zero-parameter discovery tool the guidance is concrete rather than merely implied.

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, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds genuinely non-schema behavior: newest-first ordering, open-by-default filtering, and the fact that calls do not count against the daily quota, which is useful operational context. It stops short of describing pagination mechanics.

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 purpose, then the when-to-use routing, then the quota caveat. No redundancy with the title or schema and nothing wasted.

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?

An output schema exists, so return values need no further explanation; the description still names the salient fields an agent will act on. With annotations covering safety and the description covering ordering, defaults, quota, and sibling routing, nothing an agent needs to call this 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 100%, so every parameter is already documented, including the enum and the default. The description only echoes the state default ('Shows open items unless state says otherwise') and adds nothing about limit bounds or how before/next_before paging works, so the baseline 3 applies.

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 ('List the feedback this account has sent'), declares scope (this account's own feedback to the Pressure Atlas team), ordering (newest first), and the returned fields. It is clearly distinguishable from edit_feedback, close_feedback, and send_feedback.

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 check this before sending feedback on an already-raised topic, and names edit_feedback as the alternative to avoid duplicate submissions. It also states the default filtering behavior when state is omitted, so the agent knows what it will get without reading the schema.

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, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds one real behavioral fact — that the output is result ids feeding fetch — but says nothing about result limits, ranking, 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?

Two short sentences, zero filler, and the core purpose is front-loaded before the contract detail. Every clause carries 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?

An output schema exists, so return values need not be explained, and the description still usefully flags that ids connect to fetch. For a single-parameter read tool this is nearly complete; only guidance against sibling list_datasets 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 100% and the single 'query' parameter is fully documented in the schema as free-text over names and descriptions. The description adds no syntax, scoping, or format detail beyond that, so the schema does the heavy lifting.

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 ('Search the dataset catalog') and even names the downstream consumer of its results ('usable with fetch'). It is clear, though it does not explicitly distinguish itself from the sibling list_datasets, which an agent could reasonably confuse it with.

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?

Usage is only implied: the mention that result ids are 'usable with fetch' hints at a search-then-fetch workflow, but there is no explicit when-to-use guidance or exclusion relative to list_datasets.

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 mark this as a non-read-only, non-idempotent write, and the description adds genuinely new behavior: the message is delivered to a human team, it requires explicit user consent, and it does not consume the daily quota. The rate-limit and consent semantics go well beyond what the structured fields convey.

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

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

Front-loads the action and resource, then moves through routing triggers, frequency limit, and consent requirement in three dense sentences with no filler. Every clause carries a distinct operational 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?

With an output schema present, return values need no explanation, and the description covers the remaining gaps an agent would face: consent requirement, frequency limit, quota exemption, and delivery destination. Nothing needed to invoke this 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 100%, so kind, about, and message are already fully documented including the enum values and length bounds. The description reinforces that the message should be the user's own words, but adds no format or syntax detail beyond the schema; baseline 3 applies.

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 (send) and resource (the user's feedback to the Pressure Atlas team), then enumerates the accepted kinds. This clearly demarcates it from all sibling tools, which are read-only data retrieval operations.

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 lists trigger conditions (user wants something the tools don't do, hits a limit, is confused or frustrated, expresses like/dislike), states the frequency ceiling (at most once per conversation unless the user raises it), and mandates asking the user first. Both when-to-use and when-not-to-use are covered.

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