Biomedical compound and drug intelligence — PubChem compound data (formula, SMILES, structure), OpenFDA drug labels & adverse events, and ChEMBL bioactivity & target data. Built for pharma research and biotech agents.
Get drug/compound data from PubChem (NIH). Returns CID, IUPAC name, molecular formula, molecular weight, canonical SMILES, InChI, description, and synonyms.
| Name | Required | Description | Default |
|---|---|---|---|
| cid | No | PubChem CID number (alternative to name) | |
| name | No | Drug or compound name (e.g. aspirin, caffeine, ibuprofen) | aspirin |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must convey operational behavior. It indicates the data source (PubChem/NIH) and that it returns a set of fields. However, it does not explicitly state that the operation is read-only, nor does it mention potential side effects or limitations like external API dependency or default parameter behavior.
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 description is two concise sentences, front-loaded with the primary action 'Get drug/compound data', followed by an efficient list of returned fields. There is no redundant or excessive text.
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?
Despite listing return fields, the description omits important operational details: both parameters are optional with a default of 'aspirin', there is no mention of parameter precedence or behavior when both are provided, and no output schema exists to clarify the response structure. This leaves the agent with unanswered questions for correct invocation.
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 input schema already provides 100% coverage with descriptions for both 'cid' and 'name'. The tool description does not add additional parameter semantics beyond what the schema states, 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?
The description clearly states it retrieves drug/compound data from PubChem and enumerates the specific fields returned. It distinguishes from get_drug_targets by listing data fields, but does not explicitly differentiate from get_drug_info.
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 the tool is for fetching compound data, but it does not state when to choose it over sibling tools like get_drug_info or get_drug_targets. There is no explicit guidance on parameter selection or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get drug label, safety info, adverse events, or recall data from OpenFDA. Returns brand names, generic name, indications, warnings, dosage, interactions.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Drug name (e.g. metformin, aspirin, lisinopril) | metformin |
| type | No | label (default), adverse_events, or recalls | label |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does not disclose whether this is a read-only operation, external API dependencies, rate limits, or any limitations. It only states what data it returns, which is more output than behavioral context.
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 concise sentences with no redundancy. The main action is front-loaded, followed by the return fields. Every word 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 simple tool with no output schema, the description is reasonably complete, but it does not clarify how return fields differ by 'type' (e.g., recalls vs. adverse events). This could mislead users expecting the same fields for all types.
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 schema already describes both parameters with 100% coverage, so the description's mention of types (label, adverse events, recalls) reinforces but does not add meaning beyond what is in the schema. It does not clarify parameter relationships or formatting.
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 specifies the exact resource (drug label, safety info, adverse events, recall data) and source (OpenFDA), clearly distinguishing this from sibling tools like get_compound_data or get_drug_targets. It also lists return fields, making the scope 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?
The description implies use for drug information but does not explicitly state when to choose this tool over siblings or mention exclusions. It relies on the reader to infer the use case, so there is no explicit alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get drug target and bioactivity data from ChEMBL (EMBL-EBI). Returns ChEMBL ID, max clinical phase, molecule type, molecular properties, and indication class.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Drug or compound name (e.g. ibuprofen, atorvastatin) | ibuprofen |
| type | No | molecule (default) or activity | molecule |
| chembl_id | No | ChEMBL ID for activity lookup (e.g. CHEMBL521) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It mentions the external source (ChEMBL) and return fields, but does not clarify operational details such as rate limits, error handling, or how the 'molecule' vs 'activity' modes alter behavior. Overall, it provides minimal insight beyond the function itself.
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 description is two sentences, front-loaded with the action verb, and contains no redundant information. The listed return fields are useful and earn their place, making it appropriately succinct.
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?
In the absence of an output schema, the description partially compensates by listing return fields, but it doesn't explain how the optional parameters interact or describe any limitations. For a tool with multiple modes, this leaves some gaps in the context needed for effective use.
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 schema already provides full descriptions for all three parameters (100% coverage), so the description adds no new parameter semantics. It does not explain the interplay between 'name' and 'chembl_id' or the implications of 'type=activity', but the baseline is appropriate given strong schema coverage.
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 clearly states the tool's function: fetching drug target and bioactivity data from ChEMBL, and enumerates the returned fields. It doesn't explicitly distinguish from sibling tools, but the specific data scope provides reasonable differentiation. Minor ambiguity remains around the term 'drug target' versus the listed properties.
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 guidance is provided on when to use this tool over get_compound_data or get_drug_info, nor are any prerequisites or alternative workflows mentioned. The description simply states what it does without contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool queries a distinct public database (PubChem, OpenFDA, ChEMBL) and returns clearly non-overlapping data types: chemical properties, label/safety info, and target/bioactivity. Despite the shared drug/compound domain, the descriptions leave no ambiguity about which tool to use.
All tool names follow the exact pattern 'get_' + descriptive noun phrase (compound_data, drug_info, drug_targets), making the naming scheme highly predictable and consistent.
With only three tools, the server is minimal yet well-scoped for its purpose of retrieving drug/compound information from multiple sources. Each tool adds a distinct value and the count is within the ideal range for a focused server.
The server covers three major aspects of drug information: chemical identifiers, regulatory/safety data, and target/bioactivity. However, there is no search/discovery tool to locate drugs by query, and additional areas like clinical trials or pricing are missing. Still, the core lifecycle of drug data lookup is adequately covered.