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

Annotations already declare readOnly, idempotent and non-destructive, so the safety burden is lifted. The description still adds real context: results are ranked highest-score-first, and it defines the non-obvious own_claim field (true only when the project claims it in its own README), which is not derivable from any structured field.

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, zero filler, with the core definition front-loaded and the ordering and field semantics packed into the second clause. Every phrase 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?

For a single-parameter read-only lookup with annotations covering the safety profile and no output schema, the description supplies what matters: what is returned, how it is ranked, and what the ambiguous result field means. The missing piece is routing against sibling tools.

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 parameter is fully documented in the schema, including length bounds and an example. The description's 'e.g. Cursor, ChatGPT, Notion' reinforces the expected input but adds little beyond the schema's own example. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource and scope: open-source projects that replace a named paid/closed product, with example inputs and the result ordering. An agent knows exactly what it returns. It does not, however, distinguish itself from siblings like compare or top_in_category, which could plausibly cover adjacent lookups.

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 no when-to-use guidance and never mentions the alternatives it competes with (compare, top_in_category, search_projects). The agent must infer from the name alone that this is the right tool for 'what replaces X'.

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, non-destructive behavior, so the safety profile is covered. The description adds the compared dimensions but says nothing about invalid ids, unknown projects, or latency/cost of a multi-project lookup.

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 front-loaded sentence with the constraint ('2 to 4 projects') stated first. The trailing field enumeration is long but does convey expected output content, so it earns its place.

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 no-output-schema, read-only comparison tool, listing the returned dimensions (score, place, license, Docker, GPU, etc.) gives the agent a good sense of the result. Only the failure behavior for unknown ids 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 single parameter is already documented in the schema with accepted id formats, and the description adds no syntax or format detail. The '2 to 4' phrasing merely restates the schema's minItems/maxItems, so it adds little beyond structured data.

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 ('compare') plus the resource and scope ('2 to 4 projects side by side'), and enumerates the dimensions compared. It implicitly distinguishes itself from get_project (single) and search_projects, though it never names a sibling to route away from.

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 '2 to 4 projects side by side' framing implies the use case is head-to-head evaluation, but there is no explicit when-to-use/when-not guidance and no mention of the alternatives_to sibling, which the agent must infer.

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 establish read-only, idempotent, non-destructive, closed-world behavior, so the bar is lower. The description still adds real value by defining the null sentinel ("null means Archestack does not know") and by explicitly delimiting what is NOT tracked (hosting cost, deploy templates), which prevents the agent from misreading absence as a negative answer.

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, then a dense but purposeful field enumeration, then two short boundary sentences. The long list is justified because no output schema exists, though it stretches into a run-on and could be tightened.

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

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

With no output schema, the description correctly compensates by enumerating return categories and defining null semantics, plus stating what is out of scope. Only the absence of any hint about output shape (flat vs nested, one record vs many) keeps it short of 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?

Schema coverage is 100% and the single `project` parameter is documented with three accepted identifier formats, so the schema carries the semantics. The description adds nothing about the identifier itself — baseline 3 is appropriate when 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?

The description names a specific resource — the deployment/installation facts for a single project — and enumerates the categories returned (Docker files, arm64, GPU, RAM, external services, port, README requirements, links, release/advisory upkeep). This clearly separates it from a generic project lookup, though the framing is a noun phrase rather than a verb and no sibling is named as the alternative.

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 facts an agent needs before installing or deploying one project" implies the trigger condition, but gives no explicit when-not guidance and never mentions get_project or the other siblings, so the agent must infer which tool to pick when it wants general project metadata versus deploy-readiness facts.

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, idempotentHint and non-destructive, so the safety profile is covered. Because no output schema exists, the description carries real weight by enumerating the returned payload (score, category placement, GitHub facts, health, links, review), which is genuine behavioral context beyond the annotations. It omits failure behavior such as what happens for an unknown or ambiguous project name.

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?

One front-loaded sentence opens with the scope ('Everything Archestack has on one project') before the field enumeration. The long list is dense but each element earns its place since no output schema exists to carry the return shape.

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 read tool with annotations covering safety, the description supplies the otherwise-missing return-value inventory, which is the main completeness risk with no output schema. It falls short only on sibling routing (when to use get_project vs search_projects) and on ambiguity/error behavior for the loosely specified project identifier.

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 'project' parameter already documents its three accepted forms (id, GitHub repo, plain name) with length bounds. The description adds no syntax or resolution detail beyond the schema, 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 and resource (retrieve everything on one project) and scopes it to a single project, which implicitly contrasts with the multi-project search siblings. It does not explicitly name search_projects or compare itself to a sibling, so it stops short of a 5.

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: fetch full detail for a project you already know. There is no explicit when-to-use guidance, no statement that search_projects should be called first to resolve a project, and no exclusion for cases where a cheaper sibling (e.g. deploy_facts, compare) suffices.

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, idempotent, non-destructive), so the description adds value by disclosing matching behaviour: which fields words match and the exact ranking tiebreak order. It does not mention auth, rate limits, or result shape, but ranking semantics are valuable non-schema behaviour.

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, each carrying distinct information (what it does, how matching/ranking works, how to use filters). No filler, and the core purpose is front-loaded.

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 8-parameter, zero-required search tool with no output schema, the description covers matching and ranking adequately. It could say more about result contents (e.g. what fields come back, how limit/pagination interacts), but nothing needed to invoke it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 88%, so most parameters are self-documenting. The description still adds meaning by naming the no_gpu filter explicitly and by clarifying the fields the 'query' parameter matches (names, repos, taglines, categories, replaced products), and by implying query is optional.

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 ('Find open-source AI projects') plus the scope ('across both Archestack lists') and the two mechanisms (words, filters). It is clearly distinguishable from siblings like top_in_category and get_project, which don't do word-based ranked search.

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 a concrete usage rule: 'Put hard needs in the filters, not the words (no_gpu instead of "without a GPU")'. This is genuine when-to-use guidance with a worked example. However, it never names an alternative tool or states when NOT to use this one (e.g. use get_project for a known project).

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 behavior, so the safety profile is covered. The description adds genuinely useful behavior beyond that: results are ordered best-first, each entry carries a score and tagline, and an unknown category degrades to returning the category list instead of failing.

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, front-loaded with what the tool returns, and the fallback behavior is stated compactly at the end. 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, the description correctly carries the return shape (ranking, best first, score + tagline) and the edge case. The one gap is the 'list' parameter's role in scoping the result set, which an agent must infer from the schema alone.

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 67%: 'list' documents its two allowed values and 'category' gives example ids/titles, while 'limit' has only structural constraints and no prose. The description adds the meaningful semantic that an unrecognized 'category' value returns the category list, but says nothing about how 'list' scopes the ranking or how 'limit' interacts with 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+resource ('The ranking of one category') plus the ordering and returned fields, which is more than a tautology. It does not explicitly distinguish itself from siblings like search_projects or alternatives_to, 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?

There is no statement of when to use this versus search_projects, alternatives_to, or compare — all plausible alternatives for discovery tasks. The only contextual hint is a fallback behavior for unknown categories, which is not usage guidance.

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