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

With no annotations, the description carries the full burden and does reasonably well: it discloses the caching short-circuit, the write-back side effect, the data dependencies (last seven days of news, dominant number, master-number flag, thirty-day activation window), and required infrastructure (Qdrant and an LLM endpoint). It omits failure modes and whether the write-back is idempotent or overwrites prior readings.

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 purpose and caching rule in the first two sentences, then supporting detail. The long parenthetical listing data sources is dense but earns its place; no filler sentences.

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, and the description covers inputs, side effects, caching, and infrastructure prerequisites. What remains thin is the exact trigger conditions relative to sibling tools, which an agent might still have to guess.

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?

One required parameter 'day' with 0% schema description coverage; the schema only gives a date format. The description adds 'calendar day', which confirms the semantics but adds little beyond what the name and format already imply.

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?

Opens with a specific verb+resource ('Return the numerological reading for a calendar day') and immediately distinguishes itself from siblings like compute_numerology by stating it saves the result for later calls and assembles from stored news/activations rather than computing a bare number.

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 implies usage through the caching rule ('A day that was already read answers from Qdrant without running a model'), which tells the agent this is the read-through entry point. However, it never explicitly says when to prefer this over compute_numerology, get_history, or find_patterns, so routing is left 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?

With no annotations, the description carries the full burden and does disclose that this is pure logic with no configuration, no service, and therefore no side effects or side-channel dependencies. The empty-list behavior and the deterministic counting semantics (duplicates counted) are also stated, giving a fairly complete behavioral picture for a pure function.

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 purpose in one sentence, then adds the output-shape example and edge case. The example is dense but earns its place by pinning down counting semantics; no filler sentences are present.

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 re-explained, yet the description still usefully illustrates the result shape and the duplicate-counting rule. Combined with the pure-logic note and empty-input case, it covers everything needed to invoke the tool 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 0% and the single parameter is documented only as 'array of integers' in the schema, so the description must compensate. It does so meaningfully by specifying that duplicates are counted (the [11,11] -> count=2 example) and that an empty array is accepted, clarifying semantics the schema omits.

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 (report) and resource (master numbers 11, 22, 33) and defines the domain concept, which separates it from generic siblings like compute_numerology or find_patterns. However, it never explicitly contrasts itself with those alternatives, so an agent must infer the distinction.

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 explicit guidance on when to pick this tool over siblings such as compute_numerology or find_patterns, nor any stated preconditions. The only usage hint is that an empty list is valid input, which is an edge case rather than selection 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?

With no annotations, the description carries the burden and does so reasonably: it declares the tool pure logic with no Qdrant, model, or configuration dependencies, implying a deterministic, side-effect-free computation. It also previews the output shape (raw sum, reduced value, master flag, steps). It omits any note on edge cases (e.g., non-letter characters) but covers the key behavioral profile.

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 purpose is front-loaded in the first sentence, followed by output and behavioral notes. It is efficient, though the detailed enumeration of return values is partly redundant given that an output schema exists.

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, side-effect-free computation with an output schema, the description gives enough to call it correctly: input intent, return contents, and absence of external dependencies. Missing only index/usecase routing among siblings.

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?

One string parameter with 0% schema description coverage, so the schema tells the agent nothing. The description implies the input is text to be reduced, which is essentially self-evident from the parameter name, and adds no format or handling details (case, digits, symbols). Minimum-viable rather than compensating 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?

States a specific verb and resource ('reduce a text to its numerological value') with the core transformation named up front, so the agent knows exactly what the tool computes. It does not, however, distinguish itself from the closely related sibling check_master_numbers, which also deals with master numbers.

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 explicit statement of when to use this tool versus alternatives. 'Pure logic: no Qdrant, no model, no configuration' hints at a contrast with query_qdrant, but no alternative is named and no selection condition is given.

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 no annotations, the description carries the full behavioral burden and does so well: it discloses the model+regex combination, the graceful degradation to regex on model failure, that ``sources`` reports which strategies contributed, and the LLM-endpoint prerequisite. It stops short of return format/pagination details, but the failure and dependency behavior is unusually well covered.

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 purpose, then elaborates on the combination/degradation behavior and the endpoint requirement. Every sentence carries information, though the degradation explanation is slightly verbose.

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 extraction tool with an output schema present, the description covers purpose, behavioral failure modes, and the environment prerequisite. The main gap is the lack of when-to-use guidance relative to siblings.

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% for the single required `text` parameter. The description implies text is the input source but adds no format, constraints, or examples for it, and its only structured detail (``sources``) concerns output, 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?

States a specific verb and resource: read numbers, dates and symbols out of text, via an extraction agent. Clear enough that an agent knows the tool's job, but it never contrasts itself with siblings like find_patterns or compute_numerology, so sibling differentiation is absent.

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 a usage condition (needs an LLM endpoint via OPENAI_API_KEY/OPENAI_BASE_URL) but offers no explicit when-to-use/when-not and no routing to or away from alternatives among the sibling tools. Usage is only implied by the extraction framing.

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 no annotations, the description carries the full burden and does so well: it discloses the five-source parallel fan-out, merge/de-dupe/range-filter pipeline, partial-failure semantics (a broken feed is skipped with a warning rather than failing the call), and the explicit error condition when no source is configured (noting GDELT needs no key). These are exactly the operational traits an agent cannot get 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?

Two sentences, front-loaded with the action and scope, then the pipeline/failure semantics. Slightly parenthetical in the second sentence but no wasted sentences.

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 covers configuration prerequisites and failure modes thoroughly. The remaining gap is the unexplained 'topic' parameter, which matters for a search 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 reported at 0% for the top-level properties: 'topic' has no description anywhere, so the agent gets no guidance on query syntax or multi-word behavior. The description adds only that the date range is inclusive, which the nested DateRangeInput schema already states for start/end.

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 (news) plus the scope: a topic over an inclusive date range from every configured source. Siblings (build_forecast, compute_numerology, query_qdrant) are unrelated, so no differentiation is needed for an agent to identify this as the news-retrieval tool.

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 implies when to use it (retrieving news for a topic/date range), but names no alternative tool and states no when-not conditions. Usage is inferable rather than 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?

With no annotations, the description carries the full burden and does so well: it discloses the write side effect (patterns are saved), the missing-id policy (skipped, not refused), the empty-input behavior (returns [] without an LLM call), the discovered_at provenance, and the external dependencies (Qdrant + LLM endpoint). That is unusually rich behavioral 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?

The core action is front-loaded in the first sentence, and the follow-on sentences each add distinct operational detail rather than padding. It is slightly long and stylistically loose (backticked inline code, quoted phrases), but nothing is 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?

For a one-parameter tool with an output schema, the description covers the pieces the schema cannot: side effects, error handling, dependency requirements, and return provenance. An agent has everything needed to call it correctly and interpret an empty result.

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%, so the description must compensate for the single news_ids parameter — and it does, explaining where the ids originate and how invalid or empty inputs are treated. It does not state the id format (UUID) explicitly, but the schema supplies that, so the gap is minor.

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+side effect: find connections among stored news items, save them, return them. It names siblings (fetch_news, query_qdrant, save_pattern) as the id source and timestamp origin, so an agent can place it in the workflow. It stops short of an explicit 'use this instead of X' contrast, but the purpose is unmistakable.

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 clear context of use: the ids must come from an earlier fetch_news/ingest run or query_qdrant, implying the tool is invoked downstream of ingestion. Edge cases are spelled out (unknown ids skipped, empty list short-circuits). No explicit when-not-to-use or alternative tool is named, which keeps it at 4 rather than 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?

With no annotations, the description carries the full burden and does well: it discloses ordering (newest first), entry granularity (one row per news-item/number pair), the exact field contents returned, window inclusivity (days=1 means today), and the infrastructure dependency (Qdrant only). It omits pagination/result caps and empty-result behavior, but the behavioral disclosure is substantial.

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 core semantics are front-loaded, but the sentence about the original design sketch signature ('get_history(number)') is narrative filler that doesn't help an agent invoke the tool, diluting an otherwise tight description.

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, yet the description still sketches them helpfully. With no annotations, the added ordering, dependency, and window semantics make the definition complete enough for correct invocation; only result-limit behavior is unaddressed.

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% (days documented, number not). The description compensates well for days by explaining the window ends today, is inclusive, and that the default of 30 matches the memory window; number is only implied as the value to look up, which is the residual 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 verb+resource: returns when a number was activated in news, newest first, within a day window. This is clearly distinguishable from siblings like find_patterns or fetch_news, and it names the entity granularity (news item, number) pair.

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 implied by the wording (a historical lookup over a window), but there is no explicit when-to-use/when-not guidance and no alternatives named among siblings such as find_patterns or query_qdrant. An agent can infer the intent but gets no routing help.

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 no annotations, the description carries the full disclosure burden and does well: it states the query runs against Qdrant only, that nothing is embedded remotely and no model is called, that filters are an error (not silently ignored) for patterns, that results are best-match-first, and that scores are deliberately not returned. Missing are behaviors like empty-result handling and the 1-50 limit boundary, keeping it from 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?

Front-loaded with the core action in the first sentence, then structured per-collection, then a return-shape note. The backtick-heavy prose is slightly ornate but every sentence carries information; nothing is 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?

For a 4-parameter search tool with an output schema present, this is complete: collection semantics, filter applicability, error behavior, run-time dependencies, and result ordering/typing are all covered without re-explaining the output schema.

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%, so the description must compensate, and it does by enumerating the filter dimensions ('publication window, source, reduced value or master number') and, more importantly, by scoping filters to collection='news' only. It does not discuss the limit or query parameters, so it is not fully covering 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 verb+resource ('Search the stored news or patterns by meaning') and splits the two target collections so each has a distinct meaning. An agent can tell it apart from find_patterns and fetch_news 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?

Gives explicit per-collection usage: news supports optional payload filters with enumerated dimensions, patterns takes none and rejects them as an error rather than ignoring them. It stops short of naming or routing against the similarly-named sibling tools (find_patterns, check_master_numbers), so 4 rather than 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?

No annotations exist, so the description carries full load, and it does well: idempotent upsert semantics by pattern id, an existing timestamp is never rewritten, and the server stamps discovered_at when absent when writing to Qdrant. Missing are failure modes, permission/auth requirements, and whether the write is atomic or rate-limited.

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 first sentence is front-loaded and useful. The middle sentence justifying the SavedPattern-vs-Pattern return-type naming is design rationale that does not help an agent select or invoke the tool, so it dilutes an otherwise tight definition.

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 not be described, and the description covers write semantics, idempotency, timestamp handling, and the Qdrant dependency. What remains missing for a mutation tool with zero annotations is error/permission behavior.

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?

Reported schema description coverage is 0%, though the nested $defs actually document most fields, so the description's obligations are lighter than the metric suggests. The description echoes the id-overwrite and discovered_at-stamping semantics already present in the schema rather than adding new parameter meaning, landing at the baseline 3.

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 opening sentence gives a specific verb and resource ('Store one pattern') plus the post-condition ('return it as written, with discovered_at filled in'). It is distinguishable from siblings like find_patterns and query_qdrant by the write/read split, though it never names an alternative 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?

Usage is only implied — the tool stores a single pattern, and re-saving the same id overwrites. There is no stated when-to-use versus find_patterns, no when-not, and no guidance on when saving is appropriate for an agent. 'Needs Qdrant only' is a useful dependency note but not usage routing.

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