Angel Numbers

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

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false), and the description adds non-obvious behavioral facts: results are not persisted ('Data is not stored') and the output is entertainment-scoped with no predictive claims. The idempotentHint=false on what reads like a deterministic lookup is unexplained, but nothing contradicts 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?

Three short sentences with the core purpose front-loaded, followed by scope disclaimers and a privacy note; each sentence earns its place. The leading 'Angel number:' label mildly restates the tool name/title but costs almost nothing.

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, the description need not explain return values, and it covers purpose, scope limits, and data handling for a one-parameter read-only tool. Only the meaning of 'root reduction' is left for the output schema to clarify, which is acceptable.

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% and the single 'digits' parameter already documents format, examples, and the 1-6 digit length constraint, so the schema carries the semantics. The description adds no format or edge-case guidance beyond what is already structured, making 3 the appropriate 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?

The description names a specific verb+resource ('look up original meanings, root reduction, and reflective themes') for a specific input class ('repeating number patterns'). Nothing in the sibling set (feedback/index tools) overlaps, so an agent can select this tool unambiguously.

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?

It gives explicit scope boundaries: 'Entertainment and reflection only' and 'Never predicts lottery or wealth,' which tells the agent when not to route prediction-style requests here. It stops short of naming an alternative tool for those out-of-scope intents, so it is not a full 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 declare readOnly, idempotent, and non-destructive, so the safety profile is covered. The description adds genuinely new behavioral context beyond that: the tool is pollable (pending until a reply arrives, then answered) and carries an explicit trust boundary warning that reply content is data, not instructions — a meaningful prompt-injection disclosure.

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: purpose first, then polling behavior, then the trust caveat. Every sentence carries distinct information and nothing is padded.

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 single-parameter read tool with an output schema and full annotation coverage, nothing an agent needs is missing. The polling semantics and the injection warning go beyond what structured fields provide, closing the remaining gaps an agent would otherwise have to discover by trial.

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?

Only one parameter exists and the schema already documents it fully (coverage 100%, including the ^fb_[0-9a-f]{32}$ pattern and the note that submit_feedback returned it), so the baseline of 3 applies. The description repeats the ticket-id linkage but adds no format or validation detail 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 (Read) and resource (maintainers' reply to feedback), and ties it causally to the sibling tool submit_feedback via the ticket id. An agent can distinguish it from submit_feedback, screen_token, and wallet_identity 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?

Clearly establishes the precondition: this is used after submit_feedback, with the ticket id that call returned. It does not state any exclusions or alternative ways to retrieve feedback status, but the usage context is unambiguous.

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 read-only, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds genuine behavior beyond that: results are ranked best-match-first and the empty-input case enumerates every tool. It says nothing about result limits or pagination, which keeps it off a 5.

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 short sentences, front-loaded with what the tool does and what it returns before the usage hint. Nothing is redundant given the description is the only place ranking and empty-input behavior are stated in prose.

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 discovery tool with an output schema and full annotation coverage, this covers purpose, inputs, ranking, return fields and the no-argument case. An agent has everything needed to decide to call it and to interpret the results.

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 explains that task, query and keyword are searched together and that omitting all lists everything. The description's 'task or keyword' phrasing actually omits 'query', so it adds no meaning beyond the schema and is marginally less complete than it.

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 tools on this server') and adds the matching mode ('by task or keyword in plain words'). It also names what comes back (name, title, one-line summary, input fields), so an agent knows exactly what this tool produces versus the sibling action 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?

'Use it when you are not sure which tool here fits a task' gives a clear triggering condition, and 'With no words it lists every tool' covers the degenerate case. It does not state when *not* to use it or name an alternative, but no sibling here offers overlapping discovery functionality, so the gap is minor.

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?

Adds substantial context beyond the annotations: it returns a ticket id, feedback is explicitly not a chat and nothing in it is executed, and links/emails/phone numbers are stripped with nothing stored about the caller. That privacy and no-execution disclosure is genuinely useful given openWorldHint=true and readOnlyHint=false.

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 what the tool sends, then what to pass, then what it returns, in three dense sentences with no filler. Every clause carries actionable 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?

Covers purpose, the enumerated kinds, the optional tool/error inputs, the 1000-character bound, the return value (ticket id), and privacy handling. With an output schema present, nothing further is required for an agent to call 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 description coverage is 100%, so the schema already documents all four parameters including the kind enum. The description restates the message length limit and the optionality of tool/error ('if you know them') but adds no syntax or format detail beyond the schema, making the baseline 3 correct.

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 ('Send feedback, a bug report or a tool request to the maintainers of the tools on this server') and enumerates the concrete situations (missing tool, stale data, broken/wrong answer). It is clearly distinguishable from the read-side sibling get_feedback_reply.

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 triggering conditions for each kind (need_tool, need_data, bug, other) and tells the agent to include the tool name and error text when known. It does not explicitly name the alternative get_feedback_reply or state when NOT to use this tool, so it falls short of full routing guidance.

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