Skip to main content
Glama

Compose a memory_token citation handle

emem_memory_token
Read-onlyIdempotent

Create a verifiable citation token that points to a signed fact, letting any agent resolve the exact data and avoid referential drift.

Instructions

Mint a citation handle, emem:fact:<cell64>:<fact_cid> (or :<state_cid>), that any agent or LLM resolves to the byte-identical signed object. The antidote to referential drift on the value side: hand this one string to another agent instead of re-describing the fact. Validates both components are non-empty and free of the : separator. Memory algebra: the cite operation (https://emem.dev/docs/model.html).

When to use: Call when the agent wants a single rebindable string to cite a place plus an attested fact across messages, threads, agents, or tools, without re-fetching or re-describing it. Pair with emem_verify_receipt on the receiving end to check the signed payload. To cite an OBJECT rather than a single reading, use emem_entity's emem:entity: token. FOR MANY FACTS, USE emem_memory_bundle INSTEAD, and this is a measured cost rather than a style preference. Measured over 131 scalar facts at 12 places across 57 bands: a token is 84 characters and 51 LLM tokens, while the signed value it points at averages 10.9 characters and 5.4 LLM tokens. So N individual tokens cost roughly 9.5x the CONTEXT of simply pasting the N numbers (7.7x by characters; the gap is BPE fragmenting a base32 cid, and LLM tokens are the unit that bills a window), and an N-token prompt hits the context wall SOONER than the plain values would. A bundle is 38 characters and 23 LLM tokens at ANY N up to 256 and resolves in one round trip: it beats individual tokens from N=1 and beats pasting the plain values from N>=5. Individual tokens are for citing ONE fact you must be able to verify later; they are the wrong tool for carrying a set.

Example arguments: {"cell":"defi.zb493.xoso.zcb6a","fact_cid":"cxjiu7l54ujzrpnekp24n4534yojpue4mprddbvevnqtti3lh5bq"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bandNoOptional band key. When set, the minted citation carries the band's tamper-provenance block (class, deterministic, tamper_evidence, trust_rank) so the receiving agent sees the trust class without a resolve round-trip.
cellYescell64, neither component may contain `:`.
fact_cidYes52-char base32-nopad-lowercase content-id of the fact (full 32-byte blake3).
observed_onNoThe fact's source capture date (YYYY-MM-DD) as `/v1/recall` reports it in `sources[].captured_at`. Supplied together with `band` it additionally mints the self-describing `descriptor_token`. A wrong date forges nothing: resolve binds the date to the signed fact and answers 409 on a mismatch.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
cellYes
docsNo
grammarNoThe token grammar, so the form can be parsed rather than pattern-matched.
fact_cidYes
cell_tokenNoThe address alone, when you mean the place rather than an observation of it.
memory_tokenYesThe citation to paste: emem:fact:<cell64>:<fact_cid>. Copy it verbatim; a hand-assembled token that is one character wrong still reads as a citation and resolves to nothing.
Behavior4/5

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

Annotations already declare readOnly and idempotent behavior, so the bar is lower. The description adds meaningful context beyond that: validation rules ('non-empty and free of the `:` separator'), the output format, the alternative `:<state_cid>` form, and the link to the 'cite' operation. It does not contradict annotations; it enriches the behavioral picture.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the primary purpose and format, then structured with a 'When to use' section and detailed cost analysis. The length is justified by the need for sibling differentiation and guidance on when not to use the tool, though the cost metrics are more detailed than strictly necessary.

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

Completeness5/5

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

Given the presence of an output schema and strong annotations, the description is highly complete: it explains the token format, validation, usage context, alternatives, pairing with verification, and an example. It covers all key aspects of selection and invocation for a 4-parameter tool, leaving no significant gaps.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds value by specifying the validation rule that cell and fact_cid cannot contain ':', and by providing a concrete example with realistic values. It does not cover band/observed_on in the description, but the schema already documents those adequately, so this extra is appreciated but not essential.

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

Purpose5/5

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

The description states a clear, specific action: 'Mint a citation handle, `emem:fact:<cell64>:<fact_cid>` (or `:<state_cid>`)'. It distinguishes itself from siblings by explicitly mentioning emem_entity for objects and emem_memory_bundle for many facts, so the resource and scope are unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'When to use' section provides explicit guidance: call when a single rebindable string is needed to cite a fact across contexts, and says to pair with emem_verify_receipt. It gives clear exclusions: use emem_entity for objects and emem_memory_bundle for many facts, and includes cost analysis to justify when this tool is the wrong choice.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Vortx-AI/emem'

If you have feedback or need assistance with the MCP directory API, please join our Discord server