Psilocin Human Neuron Transcriptomics

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely non-obvious behavior: all three contrasts are returned by default, missing values are included rather than omitted, and results are not a functional measurement — an important interpretive caveat. It does not describe response shape, but an output schema exists to carry that.

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?

Dense and front-loaded — the key input constraint comes first, followed by output scope and caveats. The telegraphic sentence fragments ('Exact symbol or Ensembl ID.') are efficient but slightly clipped, and the 'Returns ... missing values, source tables, citations and limitations' list reads as a run-on.

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 two-parameter read tool with a full annotation set and an output schema, the description covers input format, default scoping, and the major interpretive limitation. The only meaningful omission is routing to the search sibling when an exact identifier is unavailable.

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, and it does: it specifies that 'gene' requires an exact symbol or Ensembl ID and explains that omitting 'contrast' yields all three contrasts. The enum values themselves remain documented only by 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?

The description states the lookup key format (exact symbol or Ensembl ID) and the payload (DESeq2 estimates across three contrasts), which is enough to distinguish it from the sibling search_transcriptomic_genes by the word 'exact'. It never uses an explicit verb like 'fetch' or 'read', relying on the title for that, and it does not name the search sibling directly.

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?

'No automatic alias substitution' implies the caller must resolve aliases elsewhere, and 'unless one is specified' clarifies the default contrast behavior. However, it never explicitly says to use search_transcriptomic_genes to resolve a non-exact name, nor does it exclude any use case outright.

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 (read-only, idempotent, closed-world, non-destructive), and the description goes further by disclosing completeness behavior: all FGSEA variants, BOTH CAMERA settings where tested, nonsignificant results included, and the matching rule that spaces become underscores. The 'Not independent replication' caveat is a genuinely useful interpretation constraint that annotations cannot express; only the page-size cap merely restates 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?

Four short sentences, front-loaded with the core action and matching rule before the return behavior and the pagination constraint. Minor waste in 'Paginate at most five pathways per call', which duplicates the schema's maximum of 5.

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, yet the description still usefully summarizes return scope (all variants, both CAMERA settings, nonsignificant included) and states the non-replication caveat, which is enough for an agent to call it correctly. The remaining gap is parameter explanation for `contrast` and `offset`, which is a schema-side deficiency more than a description failure.

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 description must carry parameter meaning, and it does explain the query matching semantics (literal substring, spaces to underscores) and the five-per-call page size. However, it says nothing about the `contrast` enum (day1/day3 comparisons) or what `offset` does, leaving two of four parameters undocumented anywhere.

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 and resource ('Search frozen pathway identifiers by literal substring') plus what comes back ('all FGSEA variants and BOTH CAMERA settings'). It is clearly distinguishable from search_transcriptomic_genes by being about pathway-level results, though it never names that sibling to sharpen the contrast.

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: an agent can infer this is the lookup for pre-computed pathway enrichment results, and the closing 'Not independent replication' warns against treating output as corroborating evidence. There is no explicit when-to-use statement and no routing to get_transcriptomic_gene, get_transcriptomic_robustness, or get_transcriptomic_provenance for adjacent questions.

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, closed-world and non-destructive, so the safety profile is covered. The description adds genuinely new behavioral context: the data is a reviewed snapshot, updates require a deployment, so results can lag the live database — an important staleness signal an agent would not get from the annotations alone.

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 dense sentence with zero filler, front-loading the concrete artifacts and ending with the snapshot-vs-live distinction that matters most for interpretation. It reads as a noun-phrase list rather than a structured statement, which slightly weakens scannability but costs no 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?

An output schema exists, so return values need not be restated, and the description supplies the framing an agent needs: what categories of provenance are included, that the source is a frozen archive rather than the live database, and how refreshes happen. Nothing further is required for a zero-parameter read 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?

The tool takes no parameters, so there is no argument semantics to explain; baseline is 4. The description correctly does not invent pseudo-parameters and instead spends its words on what the parameterless call yields.

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?

Names a specific resource and enumerates its contents (Zenodo DOI, checksums, analysis commit, field definitions, pooled sample design, licences), which clearly distinguishes it from siblings like get_transcriptomic_gene or get_transcriptomic_robustness. It stops short of a verb-led statement such as 'Returns the frozen provenance record', so the agent infers the action from the name plus the inventory.

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 clause 'Separate from the live publication database' implies a when-to-use rule (consult this for archival/attribution facts, not current publication data), but no alternative sibling is named and no explicit exclusion tells the agent to prefer get_citation or get_transcriptomic_* tools for other needs. Guidance is implied rather than stated.

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 and closed-world, so the bar is lower. The description still adds substantive epistemic context beyond them: the payload includes negative results and scientific limitations, and no model fitting occurs, meaning the agent is reading precomputed artifacts rather than triggering computation.

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 text is short and dense with no filler, but it is a comma-spliced noun list that does not front-load a verb or the primary scope. Fragments like 'exploratory DNA-maintenance or targeted redox results' take effort to parse and bury the default-topic fact mid-sentence.

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 explanation, and annotations cover the safety profile; with only one optional parameter the surface is small. The remaining gap is the unexplained 'oxphos' topic value, which an agent would have to probe empirically.

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 sole 'topic' enum (all/oxphos/dna/redox) is otherwise undefined. The description recovers some of this by stating the default is 'all' and by alluding to DNA-maintenance and redox result sets, but it never explains what 'oxphos' scopes to, leaving one enum value unexplained.

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 enumerates the payload contents (CAMERA tables, ten Bayesian model summaries and diagnostics, alias sensitivity, DNA-maintenance/redox results), which makes the resource recognizable, but it never states a verb such as 'returns' or 'reads' and never contrasts itself with siblings like get_transcriptomic_pathways or get_transcriptomic_gene. The reader must infer from the title that this is a read of precomputed robustness/follow-up summaries.

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?

No explicit when-to-use guidance and no named alternatives; the only directional hints are 'topic defaults to all' and the negative constraint 'no model fitting'. There is nothing telling an agent whether to come here or to get_transcriptomic_pathways for a given question.

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, closed-world and non-destructive, so the safety profile is covered. The description adds real value beyond that: the results come from a frozen dataset version (Zenodo v1.2.0) and matches are "bounded" rather than exhaustive. It does not say how the bound interacts with limit/offset or what happens to the offset ceiling, leaving paging behavior partly opaque.

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 match-mode and dataset constraint lead, and the sibling exclusions follow, so the agent gets the discriminating information first.

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 explanation, and the description covers dataset provenance, match mode, and scope. The remaining gap is pagination semantics for limit and offset, which matters for a tool whose result set is explicitly bounded.

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 description carries the burden for three undocumented parameters. It does clarify the most important one — query is a literal gene symbol or Ensembl ID, not free text — but says nothing about limit, offset, or their interaction with the "bounded matches" claim, even though the schema caps them at 50 and 50000.

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: "Literal symbol or Ensembl-ID discovery" over "primary RNA-seq results" from a pinned Zenodo v1.2.0 dataset. It also excludes two neighboring modes (publication search, semantic search), which separates it from search_publications and search. It stops short of naming get_transcriptomic_gene, the sibling that fetches a single gene record, so the boundary there is implicit.

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 "not publication search or semantic search" clause is an explicit exclusion that tells the agent when to route elsewhere, and "literal symbol or Ensembl-ID" tells it when this tool is correct. What is missing is the positive alternative for fetching one known gene by ID (get_transcriptomic_gene) and any note on what to do when a literal match fails.

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